Support opt model (#10165)

python/sglang/srt/models/opt.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.
+# ==============================================================================
+
+"""Inference-only OPT model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import OPTConfig
+
+from sglang.srt.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from sglang.srt.layers.activation import get_act_fn
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
+from sglang.srt.layers.pooler import Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
+from sglang.srt.model_loader.weight_utils import (
+    default_weight_loader,
+    kv_cache_scales_loader,
+    maybe_remap_kv_scale_name,
+)
+from sglang.srt.utils import add_prefix, make_layers
+
+
+def get_activation(name="relu"):
+    """Select an activation function by name
+
+    Args:
+        name: str
+            activation function name,
+            one of ["relu", "gelu", "swish", "sigmoid"],
+            default "relu".
+    """
+    name = name.lower()
+    if name == "relu":
+        return nn.ReLU()
+    if name == "gelu":
+        return nn.GELU()
+    if name == "sigmoid":
+        return torch.nn.Sigmoid()
+    return nn.Identity()
+
+
+class OPTLearnedPositionalEmbedding(nn.Embedding):
+
+    def __init__(self, num_embeddings: int, embedding_dim: int):
+        # OPT is set up so that if padding_idx is specified then offset the
+        # embedding ids by 2 and adjust num_embeddings appropriately. Other
+        # models don't have this hack
+        self.offset = 2
+        super().__init__(num_embeddings + self.offset, embedding_dim)
+
+    def forward(self, positions: torch.Tensor):
+        return super().forward(positions + self.offset)
+
+
+class OPTAttention(nn.Module):
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        layer_id: int = 0,
+        bias: bool = True,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.embed_dim = embed_dim
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
+        total_num_heads = num_heads
+        assert num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = total_num_heads // tensor_model_parallel_world_size
+        self.head_dim = embed_dim // total_num_heads
+        self.scaling = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            embed_dim,
+            self.head_dim,
+            total_num_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("qkv_proj", prefix),
+        )
+        self.out_proj = RowParallelLinear(
+            embed_dim,
+            embed_dim,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("o_proj", prefix),
+        )
+
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_heads,
+            layer_id=layer_id,
+            quant_config=quant_config,
+            prefix=add_prefix("attn", prefix),
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        attn_output = self.attn(q, k, v, forward_batch)
+        output, _ = self.out_proj(attn_output)
+        return output
+
+
+class OPTDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: OPTConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.self_attn = OPTAttention(
+            embed_dim=self.embed_dim,
+            num_heads=config.num_attention_heads,
+            layer_id=layer_id,
+            bias=config.enable_bias,
+            quant_config=quant_config,
+            prefix=add_prefix("self_attn", prefix),
+        )
+        self.do_layer_norm_before = config.do_layer_norm_before
+
+        self.self_attn_layer_norm = nn.LayerNorm(
+            self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine
+        )
+        self.fc1 = ColumnParallelLinear(
+            self.embed_dim,
+            config.ffn_dim,
+            bias=config.enable_bias,
+            quant_config=quant_config,
+            prefix=add_prefix("fc1", prefix),
+        )
+        self.activation_fn = get_activation(config.activation_function)
+        self.fc2 = RowParallelLinear(
+            config.ffn_dim,
+            self.embed_dim,
+            bias=config.enable_bias,
+            quant_config=quant_config,
+            prefix=add_prefix("fc2", prefix),
+        )
+        self.final_layer_norm = nn.LayerNorm(
+            self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+        if self.do_layer_norm_before:
+            hidden_states = self.self_attn_layer_norm(hidden_states)
+        hidden_states = self.self_attn(
+            hidden_states=hidden_states, forward_batch=forward_batch
+        )
+        hidden_states = residual + hidden_states
+        # 350m applies layer norm AFTER attention
+        if not self.do_layer_norm_before:
+            hidden_states = self.self_attn_layer_norm(hidden_states)
+
+        # Fully Connected
+        residual = hidden_states
+        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+        if self.do_layer_norm_before:
+            hidden_states = self.final_layer_norm(hidden_states)
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        hidden_states = residual + hidden_states
+        # 350m applies layer norm AFTER attention
+        if not self.do_layer_norm_before:
+            hidden_states = self.final_layer_norm(hidden_states)
+        return hidden_states
+
+
+class OPTDecoder(nn.Module):
+
+    def __init__(
+        self,
+        config: OPTConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.max_target_positions = config.max_position_embeddings
+        self.vocab_size = config.vocab_size
+
+        self.pp_group = get_pp_group()
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.word_embed_proj_dim,
+            prefix=add_prefix("embed_tokens", prefix),
+        )
+        # Positional embeddings are replicated (not sharded).
+        self.embed_positions = OPTLearnedPositionalEmbedding(
+            config.max_position_embeddings, config.hidden_size
+        )
+
+        # Project out & in will be replicated if they exist.
+        if config.word_embed_proj_dim != config.hidden_size:
+            self.project_out = ReplicatedLinear(
+                config.hidden_size,
+                config.word_embed_proj_dim,
+                bias=False,
+                quant_config=quant_config,
+                prefix=add_prefix("project_out", prefix),
+            )
+        else:
+            self.project_out = None
+
+        if config.word_embed_proj_dim != config.hidden_size:
+            self.project_in = ReplicatedLinear(
+                config.word_embed_proj_dim,
+                config.hidden_size,
+                bias=False,
+                quant_config=quant_config,
+                prefix=add_prefix("project_in", prefix),
+            )
+        else:
+            self.project_in = None
+
+        # Note that the only purpose of `config._remove_final_layer_norm` is to
+        # keep backward compatibility with checkpoints that have been fine-tuned
+        # before transformers v4.20.1
+        # see https://github.com/facebookresearch/metaseq/pull/164
+        if config.do_layer_norm_before and not config._remove_final_layer_norm:
+            self.final_layer_norm = nn.LayerNorm(
+                config.hidden_size,
+                elementwise_affine=config.layer_norm_elementwise_affine,
+            )
+        else:
+            self.final_layer_norm = None
+
+        self.layers, self.start_layer, self.end_layer = make_layers(
+            config.num_hidden_layers,
+            lambda idx, prefix: OPTDecoderLayer(
+                config=config, layer_id=idx, quant_config=quant_config, prefix=prefix
+            ),
+            pp_rank=self.pp_group.rank_in_group,
+            pp_size=self.pp_group.world_size,
+            prefix="model.layers",
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+        input_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, PPProxyTensors]:
+        if self.pp_group.is_first_rank:
+            if input_embeds is None:
+                input_embeds = self.embed_tokens(input_ids)
+            pos_embeds = self.embed_positions(positions)
+            if self.project_in is not None:
+                input_embeds, _ = self.project_in(input_embeds)
+            hidden_states = input_embeds + pos_embeds
+        else:
+            assert pp_proxy_tensors is not None
+            hidden_states = pp_proxy_tensors["hidden_states"]
+
+        for layer in self.layers[self.start_layer : self.end_layer]:
+            hidden_states = layer(
+                hidden_states=hidden_states, forward_batch=forward_batch
+            )
+        if not self.pp_group.is_last_rank:
+            return PPProxyTensors({"hidden_states": hidden_states})
+        if self.final_layer_norm is not None:
+            hidden_states = self.final_layer_norm(hidden_states)
+            # 没有经过这里
+        if self.project_out is not None:
+            hidden_states, _ = self.project_out(hidden_states)
+        return hidden_states
+
+
+class OPTModel(nn.Module):
+
+    def __init__(
+        self,
+        config: OPTConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        # config = vllm_config.model_config.hf_config
+        # quant_config = vllm_config.quant_config
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.pp_group = get_pp_group()
+
+        self.decoder = OPTDecoder(
+            config=config,
+            quant_config=quant_config,
+            prefix=add_prefix("decoder", prefix),
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        pp_proxy_tensors: Optional[PPProxyTensors],
+        input_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, PPProxyTensors]:
+        return self.decoder(
+            input_ids,
+            positions,
+            pp_proxy_tensors=pp_proxy_tensors,
+            input_embeds=input_embeds,
+            forward_batch=forward_batch,
+        )
+
+    def load_kv_cache_scales(self, quantization_param_path: str) -> None:
+        tp_size = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+        for layer_idx, scaling_factor in kv_cache_scales_loader(
+            quantization_param_path,
+            tp_rank,
+            tp_size,
+            self.config.num_hidden_layers,
+            self.config.__class__.model_type,
+        ):
+            if not isinstance(self.decoder.layers[layer_idx], nn.Identity):
+                layer_self_attn = self.decoder.layers[layer_idx].self_attn
+
+            if hasattr(layer_self_attn.attn, "k_scale"):
+                layer_self_attn.attn.k_scale = scaling_factor
+                layer_self_attn.attn.v_scale = scaling_factor
+            else:
+                raise RuntimeError(
+                    "Self attention has no KV cache scaling " "factor attribute!"
+                )
+
+
+class OPTForCausalLM(nn.Module):
+    # BitandBytes specific attributes
+    # in TP, these weights are partitioned along the column dimension (dim=-1)
+    column_parallel_weights_modules = [".down_proj.", ".o_proj."]
+
+    def __init__(
+        self,
+        config: OPTConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+
+        self.model = OPTModel(
+            config=config, quant_config=quant_config, prefix=add_prefix("model", prefix)
+        )
+        if self.config.tie_word_embeddings:
+            self.lm_head = self.model.decoder.embed_tokens
+        else:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.word_embed_proj_dim,
+                prefix=add_prefix("lm_head", prefix),
+            )
+        self.logits_processor = LogitsProcessor(config)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+        self.capture_aux_hidden_states = False
+        self.pp_group = get_pp_group()
+        self.stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+        ]
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+        input_embeds: Optional[torch.Tensor] = None,
+        get_embedding: bool = False,
+    ) -> LogitsProcessorOutput:
+        hidden_states = self.model(
+            input_ids=input_ids,
+            positions=positions,
+            forward_batch=forward_batch,
+            input_embeds=input_embeds,
+            pp_proxy_tensors=pp_proxy_tensors,
+        )
+        aux_hidden_states = None
+        if self.capture_aux_hidden_states:
+            hidden_states, aux_hidden_states = hidden_states
+
+        if self.pp_group.is_last_rank:
+            if not get_embedding:
+                return self.logits_processor(
+                    input_ids,
+                    hidden_states,
+                    self.lm_head,
+                    forward_batch,
+                    aux_hidden_states=aux_hidden_states,
+                )
+            else:
+                return self.pooler(hidden_states, forward_batch)
+        else:
+            return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> None:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+
+        for name, loaded_weight in weights:
+            if name.startswith("decoder"):
+                name = name.replace("decoder.", "model.decoder.")
+            layer_id = get_layer_id(name)
+            if (
+                layer_id is not None
+                and hasattr(self.model, "start_layer")
+                and (
+                    layer_id < self.model.start_layer
+                    or layer_id >= self.model.end_layer
+                )
+            ):
+                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
+                # if is_pp_missing_parameter(name, self):
+                #     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
+                # if is_pp_missing_parameter(name, self):
+                #     continue
+                if name not in params_dict:
+                    continue
+                if name in params_dict.keys():
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+                else:
+                    logger.warning(f"Parameter {name} not found in params_dict")
+
+    @property
+    def start_layer(self):
+        return self.model.start_layer
+
+    @property
+    def end_layer(self):
+        return self.model.end_layer
+
+    def get_input_embeddings(self) -> nn.Embedding:
+        return self.model.embed_tokens
+
+    def get_module_name_from_weight_name(self, name):
+        for param_name, weight_name, shard_id, num_shard in self.stacked_params_mapping:
+            if weight_name in name:
+                return (
+                    name.replace(weight_name, param_name)[: -len(".weight")],
+                    num_shard,
+                )
+        return name[: -len(".weight")], 1
+
+    def get_num_params(self):
+        params_dict = dict(self.named_parameters())
+        return len(params_dict)
+
+    def get_weights_by_name(
+        self, name: str, truncate_size: int = 100, tp_size: int = 1
+    ) -> Optional[torch.Tensor]:
+        """Get the weights of the parameter by its name. Similar to `get_parameter` in Hugging Face.
+
+        Only used for unit test with an unoptimized performance.
+        For optimized performance, please use torch.save and torch.load.
+        """
+        try:
+            if name == "lm_head.weight" and self.config.tie_word_embeddings:
+                logger.info(
+                    "word embedding is tied for this model, return embed_tokens.weight as lm_head.weight."
+                )
+                return (
+                    self.model.embed_tokens.weight.cpu()
+                    .to(torch.float32)
+                    .numpy()
+                    .tolist()[:truncate_size]
+                )
+
+            mapped_name = name
+            mapped_shard_id = None
+            for param_name, weight_name, shard_id in self.stacked_params_mapping:
+                if weight_name in name:
+                    mapped_name = name.replace(weight_name, param_name)
+                    mapped_shard_id = shard_id
+                    break
+            params_dict = dict(self.named_parameters())
+            param = params_dict[mapped_name]
+            if mapped_shard_id is not None:
+                if mapped_shard_id in ["q", "k", "v"]:
+                    num_heads = self.config.num_attention_heads // tp_size
+                    num_kv_heads = self.config.num_attention_heads // tp_size
+                    head_dim = (
+                        self.config.hidden_size // self.config.num_attention_heads
+                    )
+                    if mapped_shard_id == "q":
+                        offset = 0
+                        size = num_heads * head_dim
+                    elif mapped_shard_id == "k":
+                        offset = num_heads * head_dim
+                        size = num_kv_heads * head_dim
+                    elif mapped_shard_id == "v":
+                        offset = (num_heads + num_kv_heads) * head_dim
+                        size = num_kv_heads * head_dim
+                    weight = param.data.narrow(0, offset, size)
+                elif mapped_shard_id in [0, 1]:
+                    intermediate_size = self.config.ffn_dim
+                    slice_size = intermediate_size // tp_size
+                    if mapped_shard_id == 0:  # gate_proj
+                        offset = 0
+                        size = slice_size
+                    elif mapped_shard_id == 1:  # up_proj
+                        offset = slice_size
+                        size = slice_size
+
+                    weight = param.data.narrow(0, offset, size)
+                else:
+                    weight = param.data
+            else:
+                weight = param.data
+            if tp_size > 1 and ("o_proj" in name or "down_proj" in name):
+                gathered_weights = [torch.zeros_like(weight) for _ in range(tp_size)]
+                torch.distributed.all_gather(gathered_weights, weight)
+                weight = torch.cat(gathered_weights, dim=1)
+            return weight.cpu().to(torch.float32).numpy().tolist()[:truncate_size]
+
+        except Exception:
+            logger.error(
+                f"Error getting weights by name {name} in OPTForCausalLM: {get_exception_traceback()}"
+            )
+            return None
+
+    def get_embed_and_head(self):
+        return self.model.embed_tokens.weight, self.lm_head.weight
+
+    def set_embed_and_head(self, embed, head):
+        del self.model.embed_tokens.weight
+        del self.lm_head.weight
+        self.model.embed_tokens.weight = embed
+        self.lm_head.weight = head
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+
+    def get_embed(self):
+        return self.model.embed_tokens.weight
+
+    def set_embed(self, embed):
+        # NOTE: If draft hidden size != target hidden size, the embed weight cannot be shared for EAGLE3
+        if (
+            hasattr(self.config, "target_hidden_size")
+            and self.config.target_hidden_size != self.config.hidden_size
+        ):
+            return
+        del self.model.embed_tokens.weight
+        self.model.embed_tokens.weight = embed
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+
+    def load_kv_cache_scales(self, quantization_param_path: str) -> None:
+        self.model.load_kv_cache_scales(quantization_param_path)
+
+
+EntryClass = [OPTForCausalLM]

test/srt/models/test_generation_models.py

@@ -77,6 +77,7 @@ ALL_MODELS = [
         trust_remote_code=True,
         skip_long_prompt=True,
     ),
+    ModelCase("facebook/opt-125m", skip_long_prompt=True),
     ModelCase(
         "nvidia/Llama-3_3-Nemotron-Super-49B-v1_5",
         tp_size=2,