Feat/support encoder model (like bert) (#4887)

python/sglang/srt/layers/attention/torch_native_backend.py

@@ -6,6 +6,7 @@ import torch
 from torch.nn.functional import scaled_dot_product_attention
 
 from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
+from sglang.srt.layers.radix_attention import AttentionType
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 
 if TYPE_CHECKING:
@@ -202,6 +203,10 @@ class TorchNativeAttnBackend(AttentionBackend):
         q_ = q.view(-1, layer.tp_q_head_num, layer.qk_head_dim)
         o_ = o.view(-1, layer.tp_q_head_num, layer.v_head_dim)
 
+        causal = True
+        if layer.is_cross_attention or layer.attn_type == AttentionType.ENCODER_ONLY:
+            causal = False
+
         self._run_sdpa_forward_extend(
             q_,
             o_,
@@ -214,7 +219,7 @@ class TorchNativeAttnBackend(AttentionBackend):
             forward_batch.extend_seq_lens,
             scaling=layer.scaling,
             enable_gqa=use_gqa,
-            causal=not layer.is_cross_attention,
+            causal=causal,
         )
         return o
 

python/sglang/srt/layers/attention/triton_backend.py

@@ -10,6 +10,7 @@ import triton.language as tl
 from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
 from sglang.srt.layers.attention.utils import create_flashinfer_kv_indices_triton
 from sglang.srt.layers.dp_attention import get_attention_tp_size
+from sglang.srt.layers.radix_attention import AttentionType
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
 from sglang.srt.utils import get_bool_env_var, get_device_core_count
 
@@ -528,6 +529,10 @@ class TritonAttnBackend(AttentionBackend):
                 layer, forward_batch.out_cache_loc, k, v
             )
 
+        causal = True
+        if layer.attn_type == AttentionType.ENCODER_ONLY:
+            causal = False
+
         self.extend_attention_fwd(
             q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
             k.contiguous(),
@@ -539,6 +544,7 @@ class TritonAttnBackend(AttentionBackend):
             self.forward_metadata.kv_indptr,
             self.forward_metadata.kv_indices,
             self.forward_metadata.custom_mask,
+            causal,
             self.forward_metadata.mask_indptr,
             self.forward_metadata.max_extend_len,
             layer.scaling,

python/sglang/srt/layers/attention/triton_ops/extend_attention.py

@@ -74,6 +74,7 @@ def _fwd_kernel(
     BLOCK_M: tl.constexpr,
     BLOCK_N: tl.constexpr,
     USE_CUSTOM_MASK: tl.constexpr,
+    IS_CAUSAL: tl.constexpr,
     SKIP_PREFIX_CUSTOM_MASK: tl.constexpr,
     STORE_TRANSPOSE: tl.constexpr,
 ):
@@ -129,6 +130,7 @@ def _fwd_kernel(
     for start_n in range(0, cur_seq_len_prefix, BLOCK_N):
         start_n = tl.multiple_of(start_n, BLOCK_N)
         mask_n = (start_n + offs_n) < cur_seq_len_prefix
+
         offs_kv_loc = tl.load(
             kv_indices + cur_seq_kv_start_idx + start_n + offs_n, mask=mask_n, other=0
         )
@@ -196,7 +198,11 @@ def _fwd_kernel(
 
     # stage 2: compute the triangle part
 
-    cur_block_m_end = tl.minimum(cur_seq_len_extend, (cur_block_m + 1) * BLOCK_M)
+    cur_block_m_end = (
+        cur_seq_len_extend
+        if not IS_CAUSAL
+        else tl.minimum(cur_seq_len_extend, (cur_block_m + 1) * BLOCK_M)
+    )
     for start_n in range(0, cur_block_m_end, BLOCK_N):
         start_n = tl.multiple_of(start_n, BLOCK_N)
         mask_n = (start_n + offs_n) < cur_block_m_end
@@ -243,12 +249,15 @@ def _fwd_kernel(
             )
             custom_mask &= mask_m[:, None] & mask_n[None, :]
             qk = tl.where(custom_mask, qk, float("-inf"))
-        else:
+        elif IS_CAUSAL:
             mask_causual = (cur_block_m * BLOCK_M + offs_m[:, None]) >= (
                 start_n + offs_n[None, :]
             )
             mask_causual &= mask_m[:, None] & mask_n[None, :]
             qk = tl.where(mask_causual, qk, float("-inf"))
+        else:
+            mask_non_causal = mask_m[:, None] & mask_n[None, :]
+            qk = tl.where(mask_non_causal, qk, float("-inf"))
 
         n_e_max = tl.maximum(tl.max(qk, 1), e_max)
         re_scale = tl.exp(e_max - n_e_max)
@@ -299,6 +308,7 @@ def extend_attention_fwd(
     kv_indptr,
     kv_indices,
     custom_mask,
+    is_causal,
     mask_indptr,
     max_len_extend,
     sm_scale=None,
@@ -411,6 +421,7 @@ def extend_attention_fwd(
         Lq=Lq,
         Lv=Lv,
         USE_CUSTOM_MASK=USE_CUSTOM_MASK,
+        IS_CAUSAL=is_causal,
         SKIP_PREFIX_CUSTOM_MASK=SKIP_PREFIX_CUSTOM_MASK,
         STORE_TRANSPOSE=_is_hip,
         num_warps=num_warps,

python/sglang/srt/layers/radix_attention.py

@@ -13,6 +13,7 @@
 # ==============================================================================
 """Radix attention."""
 
+from enum import Enum
 from typing import Optional
 
 from torch import nn
@@ -22,6 +23,18 @@ from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 
 
+class AttentionType(Enum):
+    """
+    Attention type.
+    Use string to be compatible with `torch.compile`.
+    """
+
+    # Decoder attention between previous layer Q/K/V
+    DECODER = "decoder"
+    # Encoder attention between previous layer Q/K/V
+    ENCODER_ONLY = "encoder_only"
+
+
 class RadixAttention(nn.Module):
     """
     The attention layer implementation.
@@ -39,6 +52,7 @@ class RadixAttention(nn.Module):
         sliding_window_size: int = -1,
         is_cross_attention: bool = False,
         quant_config: Optional[QuantizationConfig] = None,
+        attn_type=AttentionType.DECODER,
         prefix: str = "",
         use_irope: bool = False,
     ):
@@ -64,6 +78,7 @@ class RadixAttention(nn.Module):
             self.quant_method = quant_config.get_quant_method(self, prefix=prefix)
         if self.quant_method is not None:
             self.quant_method.create_weights(self)
+        self.attn_type = attn_type
 
     def forward(
         self,

python/sglang/srt/models/bert.py

+# SPDX-License-Identifier: Apache-2.0
+from typing import Any, Dict, Iterable, Optional, Set, Tuple
+
+import torch
+from torch import nn
+
+from sglang.srt.distributed import get_tensor_model_parallel_world_size
+from sglang.srt.layers.activation import get_act_fn
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.pooler import EmbeddingPoolerOutput, Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import AttentionType, RadixAttention
+from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+
+BertConfig = None
+
+
+class BertEmbedding(nn.Module):
+
+    def __init__(self, config: BertConfig):
+
+        super().__init__()
+        self.size = config.hidden_size
+        self.word_embeddings = VocabParallelEmbedding(
+            config.vocab_size, config.hidden_size
+        )
+        self.position_embeddings = VocabParallelEmbedding(
+            config.max_position_embeddings, config.hidden_size
+        )
+        self.token_type_embeddings = VocabParallelEmbedding(
+            config.type_vocab_size, config.hidden_size
+        )
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.position_ids = nn.Parameter(
+            torch.empty((1, config.max_position_embeddings)),
+        )
+
+        self.position_embedding_type = config.position_embedding_type
+        if self.position_embedding_type != "absolute":
+            raise ValueError(
+                "Only 'absolute' position_embedding_type" + " is supported"
+            )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        input_shape = input_ids.size()
+
+        # Input embeddings.
+        inputs_embeds = self.word_embeddings(input_ids)
+
+        # Position embeddings.
+        position_embeddings = self.position_embeddings(position_ids)
+
+        token_type_ids = torch.zeros(
+            input_shape, dtype=torch.long, device=inputs_embeds.device
+        )
+
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + token_type_embeddings + position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        return embeddings
+
+
+class BertEncoder(nn.Module):
+
+    def __init__(
+        self,
+        config: BertConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.layer = nn.ModuleList(
+            [
+                BertLayer(
+                    config=config,
+                    layer_id=layer_idx,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.layer.{layer_idx}",
+                )
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+
+    def forward(
+        self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
+    ) -> torch.Tensor:
+        for layer in self.layer:
+            hidden_states = layer(hidden_states, forward_batch)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: BertConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.attention = BertAttention(
+            hidden_size=config.hidden_size,
+            num_attention_heads=config.num_attention_heads,
+            layer_id=layer_id,
+            layer_norm_eps=config.layer_norm_eps,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attention",
+        )
+
+        self.intermediate = BertIntermediate(
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            prefix=f"{prefix}.intermediate",
+        )
+
+        self.output = BertOutput(
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            layer_norm_eps=config.layer_norm_eps,
+            quant_config=quant_config,
+            prefix=f"{prefix}.output",
+        )
+
+    def forward(self, hidden_states: torch.Tensor, forward_batch: ForwardBatch):
+        attn_output = self.attention(hidden_states, forward_batch)
+        intermediate_output = self.intermediate(attn_output)
+        output = self.output(intermediate_output, attn_output)
+        return output
+
+
+class BertAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        layer_norm_eps: float,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.self_attn = BertSelfAttention(
+            hidden_size=hidden_size,
+            num_attention_heads=num_attention_heads,
+            layer_id=layer_id,
+            quant_config=quant_config,
+            prefix=f"{prefix}.output",
+        )
+
+        self.output = BertSelfOutput(
+            hidden_size=hidden_size,
+            layer_norm_eps=layer_norm_eps,
+            quant_config=quant_config,
+            prefix=f"{prefix}.output",
+        )
+
+    def forward(
+        self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
+    ) -> torch.Tensor:
+        self_output = self.self_attn(hidden_states, forward_batch)
+        return self.output(self_output, hidden_states)
+
+
+class BertSelfAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+
+        self.total_num_heads = num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = self.total_num_heads
+        self.head_dim = self.hidden_size // self.total_num_heads
+        assert self.head_dim * self.total_num_heads == self.hidden_size
+
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=self.hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.attn = RadixAttention(
+            num_heads=self.num_heads,
+            head_dim=self.head_dim,
+            scaling=self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            layer_id=layer_id,
+            prefix=f"{prefix}.attn",
+            attn_type=AttentionType.ENCODER_ONLY,
+        )
+
+    def forward(
+        self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        output = self.attn(q, k, v, forward_batch)
+        return output
+
+
+class BertSelfOutput(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        layer_norm_eps: float,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.dense = RowParallelLinear(
+            input_size=hidden_size,
+            output_size=hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.dense",
+        )
+        self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
+
+    def forward(
+        self, hidden_states: torch.Tensor, input_tensor: torch.Tensor
+    ) -> torch.Tensor:
+        hidden_states, _ = self.dense(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertIntermediate(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.dense = ColumnParallelLinear(
+            input_size=hidden_size,
+            output_size=intermediate_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.dense",
+        )
+        self.intermediate_act_fn = get_act_fn(hidden_act)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        layer_norm_eps: float,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.dense = RowParallelLinear(
+            input_size=intermediate_size,
+            output_size=hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.dense",
+        )
+
+        self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
+
+    def forward(
+        self, hidden_states: torch.Tensor, input_tensor: torch.Tensor
+    ) -> torch.Tensor:
+        hidden_states, _ = self.dense(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertModel(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        config: BertConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.embeddings = BertEmbedding(config)
+        self.encoder = BertEncoder(
+            config=config, quant_config=quant_config, prefix=f"encoder"
+        )
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+        # self.pooler = BertPooler(config)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        get_embedding: bool = False,
+    ) -> torch.Tensor:
+        assert get_embedding == True
+        # Your tokenized IDs
+
+        hidden_states = self.embeddings(
+            input_ids=input_ids,
+            position_ids=positions,
+        )
+
+        hidden_states = self.encoder(hidden_states, forward_batch=forward_batch)
+        return self.pooler(hidden_states, forward_batch)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "query", "q"),
+            ("qkv_proj", "key", "k"),
+            ("qkv_proj", "value", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            name = name.replace("self", "self_attn")
+            if "pooler" 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)
+
+
+class Contriever(BertModel):
+    pass
+
+
+EntryClass = [BertModel, Contriever]

python/sglang/test/runners.py

@@ -51,6 +51,8 @@ NUM_TOP_LOGPROBS = 5
 def get_dtype_str(torch_dtype):
     if torch_dtype is torch.float16:
         return "float16"
+    if torch_dtype is torch.float32:
+        return "float32"
     else:
         raise NotImplementedError()
 
@@ -447,6 +449,7 @@ class SRTRunner:
         port: int = DEFAULT_PORT_FOR_SRT_TEST_RUNNER,
         lora_paths: List[str] = None,
         max_loras_per_batch: int = 4,
+        attention_backend: Optional[str] = None,
         lora_backend: str = "triton",
         disable_cuda_graph: bool = False,
         disable_radix_cache: bool = False,
@@ -487,6 +490,7 @@ class SRTRunner:
             lora_paths=lora_paths,
             max_loras_per_batch=max_loras_per_batch,
             lora_backend=lora_backend,
+            attention_backend=attention_backend,
             disable_cuda_graph=disable_cuda_graph,
             disable_radix_cache=disable_radix_cache,
             chunked_prefill_size=chunked_prefill_size,

test/srt/models/test_encoder_embedding_models.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.
+# ==============================================================================
+
+# python -m unittest test_encoder_embedding_models.TestEncoderEmbeddingModels.test_prefill_logits
+
+import multiprocessing as mp
+import random
+import time
+import unittest
+
+import torch
+from transformers import AutoConfig, AutoTokenizer
+
+from sglang.test.runners import DEFAULT_PROMPTS, HFRunner, SRTRunner
+from sglang.test.test_utils import CustomTestCase, get_similarities, is_in_ci
+
+MODELS = [("BAAI/bge-small-en", 1, 1e-5)]
+
+ATTENTION_BACKEND = ["torch_native", "triton"]
+BATCH_SIZE = [30]
+TORCH_DTYPES = [torch.float32]
+sgl_to_st_ratio = []
+
+
+class TestEncoderEmbeddingModels(CustomTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        mp.set_start_method("spawn", force=True)
+
+    def _truncate_prompts(self, prompts, model_path):
+        config = AutoConfig.from_pretrained(model_path)
+        max_length = getattr(config, "max_position_embeddings", 512) - 20
+
+        tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+        truncated_prompts = []
+        for prompt in prompts:
+            tokens = tokenizer(prompt, return_tensors="pt", truncation=False)
+            if len(tokens.input_ids[0]) > max_length:
+                truncated_text = tokenizer.decode(
+                    tokens.input_ids[0][: max_length - 1], skip_special_tokens=True
+                )
+                truncated_prompts.append(truncated_text)
+            else:
+                truncated_prompts.append(prompt)
+
+        return truncated_prompts
+
+    def assert_close_prefill_logits(
+        self,
+        prompts,
+        model_path,
+        tp_size,
+        torch_dtype,
+        prefill_tolerance,
+        attention_backend,
+        batch_size,
+    ) -> None:
+        truncated_prompts = self._truncate_prompts(prompts, model_path)
+        truncated_prompts = truncated_prompts * batch_size
+
+        with HFRunner(
+            model_path,
+            torch_dtype=torch_dtype,
+            model_type="embedding",
+        ) as hf_runner:
+            # warm up
+            hf_outputs = hf_runner.forward(truncated_prompts)
+
+            st_start_time = time.time()
+            hf_outputs = hf_runner.forward(truncated_prompts)
+            st_end_time = time.time()
+
+        with SRTRunner(
+            model_path,
+            tp_size=tp_size,
+            torch_dtype=torch_dtype,
+            model_type="embedding",
+            attention_backend=attention_backend,
+            chunked_prefill_size=-1,
+            disable_radix_cache=True,
+        ) as srt_runner:
+            # warm up
+            srt_outputs = srt_runner.forward(truncated_prompts)
+
+            sgl_start_time = time.time()
+            srt_outputs = srt_runner.forward(truncated_prompts)
+            sgl_end_time = time.time()
+
+        transformer_time = st_end_time - st_start_time
+        sgl_time = sgl_end_time - sgl_start_time
+        sgl_to_st_ratio.append(sgl_time / transformer_time)
+
+        for i in range(len(truncated_prompts)):
+            hf_logits = torch.Tensor(hf_outputs.embed_logits[i])
+            srt_logits = torch.Tensor(srt_outputs.embed_logits[i])
+
+            similarity = torch.tensor(get_similarities(hf_logits, srt_logits))
+            # If something is wrong, uncomment this to observe similarity.
+            # print("similarity diff", abs(similarity - 1))
+
+            if len(truncated_prompts[i]) <= 1000:
+                assert torch.all(
+                    abs(similarity - 1) < prefill_tolerance
+                ), "embeddings are not all close"
+
+    def test_prefill_logits(self):
+        models_to_test = MODELS
+
+        if is_in_ci():
+            models_to_test = [random.choice(MODELS)]
+
+        for model, tp_size, prefill_tolerance in models_to_test:
+            for attention_backend in ATTENTION_BACKEND:
+                for batch_size in BATCH_SIZE:
+                    for torch_dtype in TORCH_DTYPES:
+                        self.assert_close_prefill_logits(
+                            DEFAULT_PROMPTS,
+                            model,
+                            tp_size,
+                            torch_dtype,
+                            prefill_tolerance,
+                            attention_backend,
+                            batch_size,
+                        )
+
+        for i in range(len(BATCH_SIZE)):
+            print(
+                "bacth size: ",
+                BATCH_SIZE[i] * 5,
+                "sgl_time/st_time",
+                round(sgl_to_st_ratio[i], 3),
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()

test/srt/test_triton_attention_kernels.py

@@ -116,6 +116,7 @@ class TestTritonAttention(CustomTestCase):
             kv_indptr,
             kv_indices,
             custom_mask,
+            True,
             mask_indptr,
             max_len_extend,
         )
@@ -150,6 +151,7 @@ class TestTritonAttention(CustomTestCase):
             kv_indptr,
             kv_indices,
             custom_mask,
+            True,
             mask_indptr,
             max_len_extend,
         )