Add support for GPT-J

python/sglang/srt/models/gptj.py

@@ -0,0 +1,313 @@
+# Adapted from vLLM's GPT-J implementation for SGLang
+# Original Authors: The vLLM team and HuggingFace Team
+# License: Apache License, Version 2.0
+
+from typing import Iterable, List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import GPTJConfig
+
+print(" ** This model is being loaded from built-in source at IR2-project **")
+# from sglang.srt.layers.activation import get_act_fn
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+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.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+
+
+class GPTJAttention(nn.Module):
+    def __init__(
+        self,
+        layer_id: int,
+        config: GPTJConfig,
+        cache_config=None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.total_num_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_size = self.hidden_size // self.total_num_heads
+
+        self.qkv_proj = QKVParallelLinear(
+            config.hidden_size,
+            self.head_size,
+            self.total_num_heads,
+            bias=False,
+            quant_config=quant_config,
+            # prefix=f"{prefix}.qkv_proj",
+        )
+        self.out_proj = RowParallelLinear(
+            config.hidden_size,
+            config.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            # prefix=f"{prefix}.out_proj",
+        )
+
+        tp_world_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_world_size == 0
+        self.num_heads = self.total_num_heads // tp_world_size
+
+        self.scale = self.head_size**-0.5
+        assert getattr(config, "rotary", True)
+        assert config.rotary_dim % 2 == 0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
+
+        self.rotary_emb = get_rope(
+            self.head_size,
+            rotary_dim=config.rotary_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            is_neox_style=False,
+        )
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_size,
+            scaling=self.scale,
+            num_kv_heads=self.total_num_heads,
+            layer_id=layer_id,
+        )
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k = self.rotary_emb(position_ids, q, k)
+        attn_output = self.attn(q, k, v, forward_batch)
+        attn_output, _ = self.out_proj(attn_output)
+        return attn_output
+
+
+class GPTJMLP(nn.Module):
+    def __init__(
+        self,
+        intermediate_size: int,
+        config: GPTJConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        hidden_size = config.n_embd
+        self.fc_in = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=True,
+            quant_config=quant_config,
+            # prefix=f"{prefix}.fc_in",
+        )
+        self.fc_out = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            # prefix=f"{prefix}.fc_out",
+        )
+        self.act = get_act_fn(
+            config.activation_function, quant_config, intermediate_size
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc_in(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.fc_out(hidden_states)
+        return hidden_states
+
+
+class GPTJBlock(nn.Module):
+    def __init__(
+        self,
+        layer_id: int,
+        config: GPTJConfig,
+        cache_config=None,
+        quant_config: Optional[QuantizationConfig] = None,
+        # prefix: str = "",
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        inner_dim = 4 * config.n_embd if config.n_inner is None else config.n_inner
+        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+
+        self.attn = GPTJAttention(
+            layer_id,
+            config,
+            cache_config,
+            quant_config,
+            # prefix=f"{prefix}.attn",
+        )
+        self.mlp = GPTJMLP(
+            inner_dim,
+            config,
+            quant_config,
+            # prefix=f"{prefix}.mlp",
+        )
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_output = self.attn(
+            position_ids=position_ids,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+        mlp_output = self.mlp(hidden_states)
+        hidden_states = attn_output + mlp_output + residual
+        return hidden_states
+
+
+class GPTJModel(nn.Module):
+    def __init__(
+        self,
+        config: GPTJConfig,
+        cache_config=None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.n_embd
+        self.wte = VocabParallelEmbedding(
+            config.vocab_size,
+            self.embed_dim,
+            # prefix=f"{prefix}.wte",
+        )
+        self.h = nn.ModuleList(
+            [
+                GPTJBlock(i, config, cache_config, quant_config)
+                for i in range(config.n_layer)
+            ]
+        )
+        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        hidden_states = self.wte(input_ids)
+
+        for layer in self.h:
+            hidden_states = layer(
+                position_ids=position_ids,
+                hidden_states=hidden_states,
+                forward_batch=forward_batch,
+            )
+
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class GPTJForCausalLM(nn.Module):
+    def __init__(
+        self,
+        config: GPTJConfig,
+        cache_config=None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.transformer = GPTJModel(
+            config, cache_config, quant_config, prefix="transformer"
+        )
+        self.lm_head = self.transformer.wte
+        self.logits_processor = LogitsProcessor(config)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(input_ids, positions, forward_batch)
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
+        )
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        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),
+        ]
+
+        # for name, loaded_weight in weights:
+        #     if "attn.bias" in name or "attn.masked_bias" in name:
+        #         continue
+
+        #     if not name.startswith("transformer."):
+        #         name = "transformer." + name
+
+        #     param = params_dict.get(name)
+        #     if param is None:
+        #         continue
+
+        #     # Handle weight transposition for Conv1D layers
+        #     conv1d_layers = ["qkv_proj", "out_proj", "fc_in", "fc_out"]
+        #     if any(conv1d_layer in name for conv1d_layer in conv1d_layers):
+        #         if name.endswith(".weight"):
+        #             loaded_weight = loaded_weight.t()
+
+        #     weight_loader = getattr(param, "weight_loader",
+        #                             default_weight_loader)
+        #     weight_loader(param, loaded_weight)
+
+        for name, loaded_weight in weights:
+            if "attn.bias" in name or "attn.masked_bias" 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:
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+                # 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 = GPTJForCausalLM

test/srt/models/test_generation_models.py

@@ -57,6 +57,7 @@ class ModelCase:
     ModelCase("allenai/OLMo-1B-0724-hf", decode_tolerance=8e-2, skip_long_prompt=True),
     ModelCase("THUDM/glm-4-9b-chat"),
     ModelCase("openai-community/gpt2"),
+    ModelCase("EleutherAI/gpt-j-6B"),
 ]
 
 TORCH_DTYPES = [torch.float16]