Support OLMo models.

vllm/model_executor/models/__init__.py

@@ -35,6 +35,7 @@
     # transformers's mpt class has lower case
     "MptForCausalLM": ("mpt", "MPTForCausalLM"),
     "MPTForCausalLM": ("mpt", "MPTForCausalLM"),
+    "OLMoForCausalLM": ("olmo", "OLMoForCausalLM"),
     "OPTForCausalLM": ("opt", "OPTForCausalLM"),
     "PhiForCausalLM": ("phi", "PhiForCausalLM"),
     "QWenLMHeadModel": ("qwen", "QWenLMHeadModel"),

vllm/model_executor/models/olmo.py

@@ -0,0 +1,378 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/allenai/OLMo/blob/v0.2.4/olmo/model.py and
+# https://github.com/allenai/OLMo/blob/v0.2.4/hf_olmo/modeling_olmo.py
+# Copyright 2023 The vLLM team.
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+#
+# BSD 3-Clause License
+#
+# Copyright (c) 2022, Tri Dao, [email protected].
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""Inference-only OLMo model compatible with HuggingFace weights."""
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    LinearMethodBase,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size, )
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (
+    default_weight_loader,
+    hf_model_weights_iterator,
+)
+from vllm.sequence import SamplerOutput
+from vllm.transformers_utils.configs.olmo import OLMoConfig
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class SwiGLU(nn.Module):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, gate = x.chunk(2, dim=-1)
+        return F.silu(gate) * x
+
+    @property
+    def output_multiplier(self) -> float:
+        return 0.5
+
+
+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,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.d_model
+        assert config.d_model % config.n_heads == 0
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
+        )
+        self.total_num_heads = self.config.n_heads
+        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
+
+        # Layer norms.
+        self.attn_norm = nn.LayerNorm(config.d_model,
+                                      elementwise_affine=False,
+                                      bias=False)
+        # Attention input projection. Projects x -> (q, k, v)
+        self.att_proj = QKVParallelLinear(
+            config.d_model,
+            self.head_dim,
+            self.total_num_heads,
+            bias=config.include_bias,
+            linear_method=linear_method,
+        )
+
+        # Rotary embeddings.
+        if self.config.rope:
+            rope_theta = getattr(config, "rope_theta", 10000)
+            max_position_embeddings = getattr(config,
+                                              "max_position_embeddings", 8192)
+            self.rotary_emb = get_rope(
+                self.head_dim,
+                rotary_dim=self.head_dim,
+                max_position=max_position_embeddings,
+                base=rope_theta,
+            )
+        self.scaling = self.head_dim**-0.5
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
+                                   scale=self.scaling)
+
+        # Attention output projection.
+        self.attn_out = RowParallelLinear(
+            config.d_model,
+            config.d_model,
+            bias=config.include_bias,
+            linear_method=linear_method,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.attn_norm(hidden_states)
+        qkv, _ = self.att_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        if self.config.rope:
+            q, k = self.rotary_emb(positions, q, k)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
+        output, _ = self.attn_out(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,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = (config.mlp_hidden_size if config.mlp_hidden_size
+                            is not None else config.mlp_ratio * config.d_model)
+
+        # Layer norms.
+        self.ff_norm = nn.LayerNorm(config.d_model,
+                                    elementwise_affine=False,
+                                    bias=False)
+
+        # Feed-forward input projection.
+        self.ff_proj = ColumnParallelLinear(
+            config.d_model,
+            self.hidden_size,
+            bias=config.include_bias,
+            linear_method=linear_method,
+        )
+
+        # Activation function.
+        # self.act = SiluAndMul()
+        # self.act.output_multiplier = 0.5
+        self.act = SwiGLU()
+        assert (self.act.output_multiplier * self.hidden_size) % 1 == 0
+
+        # Feed-forward output projection.
+        self.ff_out = RowParallelLinear(
+            int(self.act.output_multiplier * self.hidden_size),
+            config.d_model,
+            bias=config.include_bias,
+            linear_method=linear_method,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        # Add feed-forward projection.
+        # shape: (batch_size, seq_len, d_model)
+        og_x = x
+        x = self.ff_norm(x)
+        x, _ = self.ff_proj(x)
+        x = self.act(x)
+        x, _ = self.ff_out(x)
+        x = og_x + x
+
+        return x
+
+
+class OlmoBlock(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,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        # Attention block.
+        self.attn = OlmoAttention(config, linear_method)
+
+        # MLP block.
+        self.mlp = OlmoMLP(config, linear_method)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+        # Attention block.
+        og_x = hidden_states
+        x = self.attn(positions, hidden_states, kv_cache, input_metadata)
+        x = x + og_x
+
+        # MLP block.
+        hidden_states = self.mlp(x)
+        return hidden_states
+
+
+class OlmoModel(nn.Module):
+
+    def __init__(self,
+                 config: OLMoConfig,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+
+        self.transformer = nn.ModuleDict(
+            dict(
+                wte=VocabParallelEmbedding(
+                    config.embedding_size or config.vocab_size,
+                    config.d_model,
+                ),
+                ln_f=nn.LayerNorm(config.d_model,
+                                  elementwise_affine=False,
+                                  bias=False),
+            ))
+
+        blocks = [
+            OlmoBlock(config, linear_method) for i in range(config.n_layers)
+        ]
+        if self.config.block_group_size > 1:
+            raise NotImplementedError("Block group size > 1 not supported yet")
+        else:
+            self.transformer.update({"blocks": nn.ModuleList(blocks)})
+
+        if not config.weight_tying:
+            self.transformer.update({
+                "ff_out":
+                ColumnParallelLinear(
+                    config.d_model,
+                    config.embedding_size or config.vocab_size,
+                    bias=config.include_bias,
+                    linear_method=linear_method,
+                )
+            })
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        """
+        :param input_ids: A tensor of shape `(batch_size, seq_len)`.
+        """
+        # Get embeddings of input.
+        # shape: (batch_size, seq_len, d_model)
+        x = self.transformer.wte(input_ids)  # type: ignore
+
+        # Apply blocks one-by-one.
+        for block_idx, block in enumerate(self.transformer.blocks):
+            # shape: (batch_size, seq_len, d_model)
+            x = block(
+                positions,
+                x,
+                kv_caches[block_idx],
+                input_metadata,
+            )
+
+        # Apply final layer norm.
+        # shape: (batch_size, seq_len or 1, d_model)
+        x = self.transformer.ln_f(x)  # type: ignore
+        return x
+
+
+class OLMoForCausalLM(nn.Module):
+    """
+    Extremely barebones HF model wrapper.
+    """
+
+    def __init__(self,
+                 config: OLMoConfig,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = OlmoModel(config, linear_method)
+        self.lm_head_weight = (self.model.transformer.wte.weight
+                               if config.weight_tying else
+                               self.model.transformer.ff_out.weight)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(
+            input_ids=input_ids,
+            positions=positions,
+            kv_caches=kv_caches,
+            input_metadata=input_metadata,
+        )
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(
+        self,
+        model_name_or_path: str,
+        cache_dir: Optional[str] = None,
+        load_format: str = "auto",
+        revision: Optional[str] = None,
+    ):
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            # attention
+            if ".att" in name:
+                name = name.replace(".att", ".attn.att")
+            # mlp
+            if ".ff" in name and "transformer.ff_out" not in name:
+                name = name.replace(".ff", ".mlp.ff")
+            # there is no bias in olmo
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)