[gpt-oss] Add gpt-oss bf16 support
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vllm/model_executor/models/stablelm.py
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343
vllm/model_executor/models/stablelm.py
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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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# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team.
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# All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# This code is based off the following work:
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# https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/modeling_stablelm_epoch.py
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# https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/config.json
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"""Inference-only StabeLM (https://github.com/Stability-AI/StableLM)
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model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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from typing import Optional, Union
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import torch
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from torch import nn
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from transformers import StableLmConfig
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from vllm.attention import Attention
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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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 .interfaces import SupportsPP
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from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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class StablelmMLP(nn.Module):
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def __init__(self,
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config: StableLmConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "") -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_up_proj = MergedColumnParallelLinear(
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config.hidden_size, [config.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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self.down_proj = RowParallelLinear(config.intermediate_size,
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config.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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self.act_fn = SiluAndMul()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class StablelmAttention(nn.Module):
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def __init__(self,
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config: StableLmConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "") -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_heads
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_key_value_heads = config.num_key_value_heads
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if self.total_num_key_value_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_key_value_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_key_value_heads == 0
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self.num_key_value_heads = max(
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1, self.total_num_key_value_heads // tp_size)
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self.head_dim = self.hidden_size // self.total_num_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.partial_rotary_factor = getattr(
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config, "rope_pct", getattr(config, "partial_rotary_factor", 1))
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self.scaling = self.head_dim**-0.5
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_key_value_heads * self.head_dim
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self.qkv_bias = getattr(config, "use_qkv_bias", False)
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if (self.head_dim * self.num_heads * tp_size) != self.hidden_size:
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raise ValueError(f"hidden_size must be divisible by num_heads "
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f"(got `hidden_size`: {self.hidden_size}"
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f" and `num_heads`: {self.num_heads}).")
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self.qkv_proj = QKVParallelLinear(self.hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_key_value_heads,
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self.qkv_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj")
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self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
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self.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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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=self.config.max_position_embeddings,
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base=self.config.rope_theta,
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partial_rotary_factor=self.partial_rotary_factor,
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)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_key_value_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn")
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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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) -> 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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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 StablelmDecoderLayer(nn.Module):
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def __init__(
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self,
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config: StableLmConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.self_attn = StablelmAttention(config,
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cache_config,
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quant_config,
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prefix=f"{prefix}.self_attn")
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self.mlp = StablelmMLP(config, quant_config, prefix=f"{prefix}.mlp")
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norm_eps = getattr(config, "norm_eps",
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getattr(config, "layer_norm_eps", 1e-05))
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self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
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self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
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eps=norm_eps)
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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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) -> tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states, residual
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class StableLMEpochModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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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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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=f"{prefix}.embed_tokens",
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)
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: StablelmDecoderLayer(
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config, cache_config, quant_config, prefix=prefix),
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prefix=f"{prefix}.layers",
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)
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norm_eps = getattr(config, "norm_eps",
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getattr(config, "layer_norm_eps", 1e-05))
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self.norm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(["hidden_states"],
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config.hidden_size))
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors],
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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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.get_input_embeddings(input_ids)
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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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for layer in self.layers[self.start_layer:self.end_layer]:
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hidden_states, residual = layer(positions, hidden_states)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({"hidden_states": hidden_states})
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hidden_states = self.norm(hidden_states)
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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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:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[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, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class StablelmForCausalLM(nn.Module, SupportsPP):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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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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self.config = config
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self.quant_config = quant_config
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self.model = StableLMEpochModel(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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self.lm_head = ParallelLMHead(config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=f"{prefix}.lm_head")
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if self.config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.model(input_ids, positions, intermediate_tensors,
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inputs_embeds)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[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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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights)
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