From a5114b6f910f3c2a45b628a4052d47c9b518ccea Mon Sep 17 00:00:00 2001 From: Jani Monoses Date: Wed, 16 Oct 2024 10:11:18 +0300 Subject: [PATCH] Add OLMo model (#1676) --- python/sglang/srt/models/olmo.py | 352 ++++++++++++++++++++++ test/srt/models/test_generation_models.py | 6 +- 2 files changed, 357 insertions(+), 1 deletion(-) create mode 100755 python/sglang/srt/models/olmo.py mode change 100644 => 100755 test/srt/models/test_generation_models.py diff --git a/python/sglang/srt/models/olmo.py b/python/sglang/srt/models/olmo.py new file mode 100755 index 00000000000..c1557ce8da4 --- /dev/null +++ b/python/sglang/srt/models/olmo.py @@ -0,0 +1,352 @@ +""" +Copyright 2023-2024 SGLang Team +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +# Adapted from +# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/olmo.py#L1 +"""Inference-only OLMo model compatible with HuggingFace weights.""" +from typing import Iterable, List, Optional, Tuple + +import torch +from torch import nn +from transformers import OlmoConfig +from vllm.distributed import get_tensor_model_parallel_world_size +from vllm.model_executor.layers.linear import ( + MergedColumnParallelLinear, + QKVParallelLinear, + RowParallelLinear, +) +from vllm.model_executor.layers.rotary_embedding import get_rope +from vllm.model_executor.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) +from vllm.model_executor.model_loader.weight_utils import default_weight_loader + +from sglang.srt.layers.activation import SiluAndMul +from sglang.srt.layers.logits_processor import LogitsProcessor +from sglang.srt.layers.quantization.base_config import QuantizationConfig +from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.model_executor.forward_batch_info import ForwardBatch + + +class OlmoAttention(nn.Module): + """ + This is the attention block where the output is computed as + ``Attention(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))`` + (plus another skip connection). + """ + + def __init__( + self, + config: OlmoConfig, + layer_id: int = 0, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + tensor_model_parallel_world_size = get_tensor_model_parallel_world_size() + self.total_num_heads = config.num_attention_heads + + assert self.hidden_size % self.total_num_heads == 0 + assert self.total_num_heads % tensor_model_parallel_world_size == 0 + + self.num_heads = self.total_num_heads // tensor_model_parallel_world_size + self.head_dim = self.hidden_size // self.total_num_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.clip_qkv = config.clip_qkv + + # Attention input projection. Projects x -> (q, k, v) + self.qkv_proj = QKVParallelLinear( + self.hidden_size, + self.head_dim, + self.total_num_heads, + bias=config.attention_bias, + ) + + # Rotary embeddings. + self.rotary_emb = get_rope( + self.head_dim, + rotary_dim=self.head_dim, + max_position=self.max_position_embeddings, + base=self.rope_theta, + ) + self.scaling = self.head_dim**-0.5 + self.attn = RadixAttention( + self.num_heads, + self.head_dim, + self.scaling, + num_kv_heads=self.num_heads, + layer_id=layer_id, + ) + + # Attention output projection. + self.o_proj = RowParallelLinear( + self.hidden_size, + self.hidden_size, + bias=config.attention_bias, + ) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + forward_batch: ForwardBatch, + ) -> torch.Tensor: + qkv, _ = self.qkv_proj(hidden_states) + if self.clip_qkv is not None: + qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv) + q, k, v = qkv.chunk(chunks=3, dim=-1) + q, k = self.rotary_emb(positions, q, k) + attn_output = self.attn(q, k, v, forward_batch) + output, _ = self.o_proj(attn_output) + return output + + +class OlmoMLP(nn.Module): + """ + This is the MLP block where the output is computed as + ``MLP(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))`` + (plus another skip connection). + """ + + def __init__( + self, + config: OlmoConfig, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + + # Feed-forward input projection. + self.gate_up_proj = MergedColumnParallelLinear( + self.hidden_size, + [self.intermediate_size] * 2, + bias=False, + quant_config=quant_config, + ) + + # Activation function. + self.act_fn = SiluAndMul() + + # Feed-forward output projection. + self.down_proj = RowParallelLinear( + self.intermediate_size, + self.hidden_size, + bias=False, + quant_config=quant_config, + ) + + def forward( + self, + x: torch.Tensor, + ) -> torch.Tensor: + gate_up, _ = self.gate_up_proj(x) + x = self.act_fn(gate_up) + x, _ = self.down_proj(x) + return x + + +class OlmoDecoderLayer(nn.Module): + """ + This is a typical transformer block where the output is + computed as ``MLP(LN(x + Attention(LN(x))))`` + (plus another skip connection). + """ + + def __init__( + self, + config: OlmoConfig, + layer_id: int = 0, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + # Attention block. + self.self_attn = OlmoAttention(config, layer_id, quant_config) + + # MLP block. + self.mlp = OlmoMLP(config, quant_config) + + # LayerNorm + self.input_layernorm = nn.LayerNorm( + config.hidden_size, elementwise_affine=False, bias=False + ) + self.post_attention_layernorm = nn.LayerNorm( + config.hidden_size, elementwise_affine=False, bias=False + ) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + forward_batch: ForwardBatch, + ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]: + # Attention block. + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + hidden_states = self.self_attn(positions, hidden_states, forward_batch) + hidden_states = hidden_states + residual + + # MLP block. + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +class OlmoModel(nn.Module): + + def __init__( + self, config: OlmoConfig, quant_config: Optional[QuantizationConfig] = None + ): + super().__init__() + self.config = config + + self.embed_tokens = VocabParallelEmbedding( + config.vocab_size, config.hidden_size + ) + self.layers = nn.ModuleList( + [ + OlmoDecoderLayer(config, layer_idx, quant_config) + for layer_idx in range(config.num_hidden_layers) + ] + ) + self.norm = nn.LayerNorm( + config.hidden_size, elementwise_affine=False, bias=False + ) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + forward_batch: ForwardBatch, + input_embeds: torch.Tensor = None, + ) -> torch.Tensor: + """ + :param input_ids: A tensor of shape `(batch_size, seq_len)`. + """ + # Get embeddings of input. + # shape: (batch_size, seq_len, d_model) + + if input_embeds is None: + hidden_states = self.embed_tokens(input_ids) + else: + hidden_states = input_embeds + + # Apply blocks one-by-one. + for layer_idx, decoder_layer in enumerate(self.layers): + # shape: (batch_size, seq_len, d_model) + hidden_states = decoder_layer( + positions, + hidden_states, + forward_batch, + ) + + # Apply final layer norm. + # shape: (batch_size, seq_len or 1, d_model) + hidden_states = self.norm(hidden_states) + return hidden_states + + +class OlmoForCausalLM(nn.Module): + """ + Extremely barebones HF model wrapper. + """ + + def __init__( + self, + config: OlmoConfig, + cache_config=None, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.config = config + self.model = OlmoModel(config, quant_config) + if config.tie_word_embeddings: + self.lm_head = self.model.embed_tokens + else: + self.unpadded_vocab_size = config.vocab_size + self.lm_head = ParallelLMHead( + self.unpadded_vocab_size, + config.hidden_size, + org_num_embeddings=config.vocab_size, + quant_config=quant_config, + ) + self.logits_processor = LogitsProcessor(config) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + forward_batch: ForwardBatch, + input_embeds: torch.Tensor = None, + ) -> torch.Tensor: + hidden_states = self.model( + input_ids=input_ids, + positions=positions, + forward_batch=forward_batch, + input_embeds=input_embeds, + ) + return self.logits_processor( + input_ids, hidden_states, self.lm_head.weight, forward_batch + ) + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + stacked_params_mapping = [ + # (param_name, shard_name, shard_id) + ("qkv_proj", "q_proj", "q"), + ("qkv_proj", "k_proj", "k"), + ("qkv_proj", "v_proj", "v"), + ("gate_up_proj", "gate_proj", 0), + ("gate_up_proj", "up_proj", 1), + ] + params_dict = dict(self.named_parameters(remove_duplicate=False)) + for name, loaded_weight in weights: + if "rotary_emb.inv_freq" in name: + continue + if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name: + # Models trained using ColossalAI may include these tensors in + # the checkpoint. Skip them. + continue + # With tie_word_embeddings, we can skip lm_head.weight + # The weight might appear unnecessarily in the files if the model is + # processed with quantization, LoRA, fine-tuning, etc. + if self.config.tie_word_embeddings and "lm_head.weight" in 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) + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + continue + param = params_dict[name] + weight_loader = param.weight_loader + weight_loader(param, loaded_weight, shard_id) + break + else: + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + continue + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", default_weight_loader) + weight_loader(param, loaded_weight) + + +EntryClass = OlmoForCausalLM diff --git a/test/srt/models/test_generation_models.py b/test/srt/models/test_generation_models.py old mode 100644 new mode 100755 index 802f40d7d73..0a6532cd040 --- a/test/srt/models/test_generation_models.py +++ b/test/srt/models/test_generation_models.py @@ -53,6 +53,7 @@ class ModelCase: ModelCase("Qwen/Qwen2-1.5B"), ModelCase("Qwen/Qwen2.5-14B-Instruct"), ModelCase("HuggingFaceTB/SmolLM-135M-Instruct"), + ModelCase("allenai/OLMo-1B-0724-hf", decode_tolerance=8e-2), ] TORCH_DTYPES = [torch.float16] @@ -153,7 +154,10 @@ def test_others(self): # Skip long prompts for models that does not have a long context prompts = DEFAULT_PROMPTS - if model_case.model_path in ["HuggingFaceTB/SmolLM-135M-Instruct"]: + if model_case.model_path in [ + "HuggingFaceTB/SmolLM-135M-Instruct", + "allenai/OLMo-1B-0724-hf", + ]: prompts = [p for p in DEFAULT_PROMPTS if len(p) < 1000] # Assert the logits and output strs are close