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Add GPT-OSS model code and config [1/N] (#22327)
Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
This commit is contained in:
Woosuk Kwon
GitHub
parent
796bae07c5
commit
de98252f49
@ -197,6 +197,7 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
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{"6b": "EleutherAI/gpt-j-6b"}),
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"GPTNeoXForCausalLM": _HfExamplesInfo("EleutherAI/pythia-70m",
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{"1b": "EleutherAI/pythia-1.4b"}),
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"GptOssForCausalLM": _HfExamplesInfo("openai/gpt-oss-20b"),
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"GraniteForCausalLM": _HfExamplesInfo("ibm/PowerLM-3b"),
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"GraniteMoeForCausalLM": _HfExamplesInfo("ibm/PowerMoE-3b"),
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"GraniteMoeHybridForCausalLM": _HfExamplesInfo("ibm-granite/granite-4.0-tiny-preview"), # noqa: E501
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@ -247,6 +247,34 @@ class GraniteMoeHybridModelConfig(VerifyAndUpdateConfig):
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config.max_model_len)
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class GptOssConfig(VerifyAndUpdateConfig):
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@staticmethod
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def verify_and_update_config(vllm_config: "VllmConfig") -> None:
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decoding_config = vllm_config.decoding_config
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if decoding_config.reasoning_backend == "":
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decoding_config.reasoning_backend = "openai"
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# Increase the max capture size from 512 to 1024 for performance.
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# NOTE(woosuk): This will increase the number of CUDA graphs
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# from 67 to 83.
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scheduler_config = vllm_config.scheduler_config
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if len(scheduler_config.cuda_graph_sizes) == 1:
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max_capture_size = scheduler_config.cuda_graph_sizes[0]
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# FIXME(woosuk): When using full cuda graph with FA3, the max
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# supported size is 992.
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if max_capture_size < 1024:
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cuda_graph_sizes = [1, 2, 4]
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# Step size 8 for small batch sizes
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cuda_graph_sizes += [i for i in range(8, 256, 8)]
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# Step size 16 for larger batch sizes
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cuda_graph_sizes += [i for i in range(256, 1025, 16)]
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scheduler_config.cuda_graph_sizes = cuda_graph_sizes
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logger.info(
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"Overriding max cuda graph capture size to "
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"%d for performance.", 1024)
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class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
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@classmethod
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@ -345,4 +373,5 @@ MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = {
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"JinaVLForRanking": JinaVLForSequenceClassificationConfig,
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"JambaForSequenceClassification": JambaForSequenceClassificationConfig,
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"GraniteMoeHybridForCausalLM": GraniteMoeHybridModelConfig,
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"GptOssForCausalLM": GptOssConfig,
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}
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472
vllm/model_executor/models/gpt_oss.py
Normal file
472
vllm/model_executor/models/gpt_oss.py
Normal file
@ -0,0 +1,472 @@
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# 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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from typing import Optional
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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 import envs
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from vllm.attention import Attention, AttentionType
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import (get_ep_group, get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size)
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (QKVParallelLinear,
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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.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from vllm.utils import cdiv
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from .utils import extract_layer_index, maybe_prefix
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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: Optional[QuantizationConfig] = None,
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cache_config: Optional[CacheConfig] = 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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rotary_dim=self.head_dim,
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max_position=config.max_position_embeddings,
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base=config.rope_theta,
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dtype=torch.float32,
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rope_scaling={
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"rope_type":
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"yarn",
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"factor":
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config.rope_scaling["factor"],
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"original_max_position_embeddings":
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config.rope_scaling["original_max_position_embeddings"],
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"beta_fast":
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config.rope_ntk_beta,
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"beta_slow":
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config.rope_ntk_alpha,
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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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attention_sink_dtype = (
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torch.float32 if envs.VLLM_USE_TRTLLM_CONTEXT_ATTENTION
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or envs.VLLM_USE_TRTLLM_DECODE_ATTENTION else torch.bfloat16)
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self.sinks = torch.nn.Parameter(
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torch.empty(config.num_attention_heads // tp_size,
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dtype=attention_sink_dtype,
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requires_grad=False))
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self.norm = RMSNorm(config.hidden_size, eps=1e-5)
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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.rope_theta = config.rope_theta
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self.qkv = 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 %
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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(self, hidden_states: torch.Tensor,
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positions: torch.Tensor) -> torch.Tensor:
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t = self.norm(hidden_states)
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qkv, _ = self.qkv(t)
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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 + hidden_states
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class MLPBlock(torch.nn.Module):
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def __init__(
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self,
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config: GptOssConfig,
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layer_idx: int,
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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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self.layer_idx = layer_idx
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self.num_experts = config.num_local_experts
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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.norm = RMSNorm(config.hidden_size, eps=1e-5)
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self.router = torch.nn.Linear(config.hidden_size,
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config.num_local_experts,
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dtype=torch.bfloat16)
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assert config.intermediate_size % self.world_size == 0
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self.experts = FusedMoE(num_experts=config.num_local_experts,
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top_k=config.num_experts_per_token,
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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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def forward(self, x: torch.Tensor) -> torch.Tensor:
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t = self.norm(x)
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g = self.router(t)
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t = self.experts(hidden_states=t, router_logits=g)
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return x + t
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class TransformerBlock(torch.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,
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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.attn = OAIAttention(config, prefix=f"{prefix}.attn")
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self.mlp = MLPBlock(config,
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self.layer_idx,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp")
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def forward(self, hidden_states: torch.Tensor,
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positions: torch.Tensor) -> torch.Tensor:
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attn_output = self.attn(hidden_states, positions)
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output = self.mlp(attn_output)
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return output
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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.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.layers = torch.nn.ModuleList([
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TransformerBlock(
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self.config,
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quant_config=self.quant_config,
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prefix=maybe_prefix(prefix, f"block.{layer_idx}"),
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) for layer_idx in range(self.config.num_hidden_layers)
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])
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self.norm = RMSNorm(self.config.hidden_size, eps=1e-5)
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def forward(self, input_ids: torch.Tensor,
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positions: torch.Tensor) -> torch.Tensor:
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x = self.embedding(input_ids)
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for layer in self.layers:
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x = layer(x, positions)
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x = self.norm(x)
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return x
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class GptOssForCausalLM(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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self.vllm_config = vllm_config
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self.model_config = vllm_config.model_config.hf_config
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self.model = GptOssModel(
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vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"),
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)
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self.lm_head = ParallelLMHead(
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self.model_config.vocab_size,
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self.model_config.hidden_size,
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)
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self.logits_processor = LogitsProcessor(self.model_config.vocab_size)
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def forward(self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None) -> torch.Tensor:
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assert intermediate_tensors is None
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assert inputs_embeds is None
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return self.model(input_ids, positions)
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def compute_logits(self, hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> torch.Tensor:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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rename_mapping = {
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"self_attn": "attn",
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"input_layernorm.weight": "attn.norm.weight",
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"post_attention_layernorm.weight": "mlp.norm.weight",
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"embed_tokens": "embedding",
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}
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def maybe_rename(name: str) -> str:
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for remap_name, new_name in rename_mapping.items():
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if remap_name in name:
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return name.replace(remap_name, new_name)
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return name
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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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tp_rank = get_tensor_model_parallel_rank()
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tp_size = get_tensor_model_parallel_world_size()
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intermediate_size = self.model_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,
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tp_size)
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per_rank_intermediate_size = (per_rank_intermediate_size_block *
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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,
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intermediate_size)
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# Attention heads per rank
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heads_per_rank = self.model_config.num_attention_heads // tp_size
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head_start = tp_rank * heads_per_rank
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use_ep = self.vllm_config.parallel_config.enable_expert_parallel
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ep_size = get_ep_group().world_size
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ep_rank = get_ep_group().rank
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num_experts = self.model_config.num_local_experts
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experts_per_rank = num_experts // ep_size
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ep_rank_start = ep_rank * experts_per_rank
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ep_rank_end = (ep_rank + 1) * experts_per_rank
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for name, weight in weights:
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# FIXME(woosuk): Remove this after testing.
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weight = weight.cuda()
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if "gate_up_proj_blocks" in name:
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# Handle MLP gate and up projection weights
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new_name = name.replace("gate_up_proj_blocks", "w13_weight")
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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(num_experts, 2 * intermediate_size,
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-1).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[:,
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2 * tp_rank_start:2 * tp_rank_end,
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...]
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param = params_dict[new_name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param,
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narrow_weight,
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weight_name=new_name,
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shard_id=None,
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expert_id=None)
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loaded_params.add(new_name)
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elif "down_proj_blocks" in name:
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# Handle MLP down projection weights
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new_name = name.replace("down_proj_blocks", "w2_weight")
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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(num_experts, -1,
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intermediate_size // 2).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[...,
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tp_rank_start // 2:tp_rank_end // 2]
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param = params_dict[new_name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param,
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narrow_weight,
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weight_name=new_name,
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shard_id=None,
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expert_id=None)
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loaded_params.add(new_name)
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elif "gate_up_proj_scales" in name:
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# Handle MLP gate and up projection weights scale
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new_name = name.replace("gate_up_proj_scales",
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"w13_weight_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[:,
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2 * tp_rank_start:2 * tp_rank_end,
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...]
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param = params_dict[new_name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
weight_loader(param,
|
||||
narrow_weight,
|
||||
weight_name=new_name,
|
||||
shard_id=None,
|
||||
expert_id=None)
|
||||
loaded_params.add(new_name)
|
||||
|
||||
elif "down_proj_scales" in name:
|
||||
# Handle MLP down projection weights
|
||||
new_name = name.replace("down_proj_scales", "w2_weight_scale")
|
||||
if use_ep:
|
||||
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
|
||||
else:
|
||||
narrow_weight = weight[..., tp_rank_start //
|
||||
mxfp4_block:tp_rank_end //
|
||||
mxfp4_block]
|
||||
|
||||
param = params_dict[new_name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
weight_loader(param,
|
||||
narrow_weight,
|
||||
weight_name=new_name,
|
||||
shard_id=None,
|
||||
expert_id=None)
|
||||
loaded_params.add(new_name)
|
||||
elif "gate_up_proj_bias" in name:
|
||||
# Handle MLP gate and up projection biases
|
||||
new_name = name.replace("gate_up_proj_bias", "w13_bias")
|
||||
|
||||
# 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[new_name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
weight_loader(param,
|
||||
narrow_weight,
|
||||
weight_name=new_name,
|
||||
shard_id=None,
|
||||
expert_id=None)
|
||||
loaded_params.add(new_name)
|
||||
|
||||
elif "down_proj_bias" in name:
|
||||
# Handle MLP down projection bias
|
||||
new_name = name.replace("down_proj_bias", "w2_bias")
|
||||
param = params_dict[new_name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
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_()
|
||||
weight_loader(param,
|
||||
weight,
|
||||
weight_name=new_name,
|
||||
shard_id=None,
|
||||
expert_id=None)
|
||||
loaded_params.add(new_name)
|
||||
elif "sinks" in name:
|
||||
# Handle attention sinks (distributed across ranks)
|
||||
name = name.replace("self_attn", "attn")
|
||||
param = params_dict[name]
|
||||
narrow_weight = weight.narrow(0, head_start, heads_per_rank)
|
||||
param.data.copy_(narrow_weight)
|
||||
loaded_params.add(name)
|
||||
elif "q_proj" in name or "k_proj" in name or "v_proj" in name:
|
||||
shard_id = ("q" if "q_proj" in name else
|
||||
"k" if "k_proj" in name else "v")
|
||||
name = name.replace("self_attn", "attn")
|
||||
param_name = name.replace(f"{shard_id}_proj", "qkv")
|
||||
param = params_dict[param_name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, weight, loaded_shard_id=shard_id)
|
||||
loaded_params.add(param_name)
|
||||
else:
|
||||
# Handle all other weights with potential renaming
|
||||
renamed_name = maybe_rename(name)
|
||||
if renamed_name not in params_dict:
|
||||
continue
|
||||
param = params_dict[renamed_name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
weight_loader(param, weight)
|
||||
loaded_params.add(renamed_name)
|
||||
|
||||
return loaded_params
|
||||
@ -74,6 +74,7 @@ _TEXT_GENERATION_MODELS = {
|
||||
"GlmForCausalLM": ("glm", "GlmForCausalLM"),
|
||||
"Glm4ForCausalLM": ("glm4", "Glm4ForCausalLM"),
|
||||
"Glm4MoeForCausalLM": ("glm4_moe", "Glm4MoeForCausalLM"),
|
||||
"GptOssForCausalLM": ("gpt_oss", "GptOssForCausalLM"),
|
||||
"GPT2LMHeadModel": ("gpt2", "GPT2LMHeadModel"),
|
||||
"GPTBigCodeForCausalLM": ("gpt_bigcode", "GPTBigCodeForCausalLM"),
|
||||
"GPTJForCausalLM": ("gpt_j", "GPTJForCausalLM"),
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user