Add OLMo model (#1676)

python/sglang/srt/models/olmo.py

+"""
+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

test/srt/models/test_generation_models.py

@@ -53,6 +53,7 @@ ALL_OTHER_MODELS = [
     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 @@ class TestGenerationModels(unittest.TestCase):
 
             # 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