feat: use FlashInfer rmsnorm and silu

python/sglang/srt/layers/activation.py

@@ -0,0 +1,29 @@
+"""
+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.
+"""
+
+import torch
+import torch.nn as nn
+from flashinfer.activation import silu_and_mul
+
+
+class SiluAndMul(nn.Module):
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        return F.silu(x[..., :d]) * x[..., d:]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        silu_and_mul(x, out)
+        return out

python/sglang/srt/layers/layernorm.py

@@ -0,0 +1,62 @@
+"""
+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.
+"""
+
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from flashinfer.norm import fused_add_rmsnorm, rmsnorm
+
+
+class RMSNorm(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        eps: float = 1e-6,
+    ) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+
+        if residual is not None:
+            fused_add_rmsnorm(x, residual, self.weight.data, self.variance_epsilon)
+            return x, residual
+        out = rmsnorm(x, self.weight.data, self.variance_epsilon)
+        return out
+
+    def forward_native(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        orig_dtype = x.dtype
+        x = x.to(torch.float32)
+        if residual is not None:
+            x = x + residual.to(torch.float32)
+            residual = x.to(orig_dtype)
+
+        variance = x.pow(2).mean(dim=-1, keepdim=True)
+        x = x * torch.rsqrt(variance + self.variance_epsilon)
+        x = x.to(orig_dtype) * self.weight
+        if residual is None:
+            return x
+        else:
+            return x, residual

python/sglang/srt/models/internlm2.py

@@ -23,8 +23,6 @@
 from transformers import PretrainedConfig
 from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
-from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
     QKVParallelLinear,
@@ -38,13 +36,14 @@
 )
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.model_executor.forward_batch_info import InputMetadata
 
 
 class InternLM2MLP(nn.Module):
-
     def __init__(
         self,
         hidden_size: int,
@@ -74,7 +73,6 @@ def forward(self, x):
 
 
 class InternLM2Attention(nn.Module):
-
     def __init__(
         self,
         hidden_size: int,
@@ -150,7 +148,6 @@ def forward(
 
 
 class InternLMDecoderLayer(nn.Module):
-
     def __init__(
         self,
         config: PretrainedConfig,
@@ -207,7 +204,6 @@ def forward(
 
 
 class InternLM2Model(nn.Module):
-
     def __init__(
         self,
         config: PretrainedConfig,
@@ -254,7 +250,6 @@ def forward(
 
 
 class InternLM2ForCausalLM(nn.Module):
-
     def __init__(
         self,
         config: PretrainedConfig,

python/sglang/srt/models/llama2.py

@@ -24,8 +24,6 @@
 from transformers import LlamaConfig
 from vllm.config import CacheConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
-from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
     QKVParallelLinear,
@@ -39,6 +37,8 @@
 )
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.logits_processor import LogitProcessorOutput, LogitsProcessor
 from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.model_executor.forward_batch_info import InputMetadata

python/sglang/srt/server.py

@@ -384,7 +384,7 @@ def _set_envs_and_config(server_args: ServerArgs):
     if not server_args.disable_flashinfer:
         assert_pkg_version(
             "flashinfer",
-            "0.1.3",
+            "0.1.4",
             "Please uninstall the old version and "
             "reinstall the latest version by following the instructions "
             "at https://docs.flashinfer.ai/installation.html.",

python/sglang/test/test_layernorm.py

@@ -0,0 +1,60 @@
+import itertools
+import unittest
+
+import torch
+
+from sglang.srt.layers.layernorm import RMSNorm
+
+
+class TestRMSNorm(unittest.TestCase):
+    DTYPES = [torch.half, torch.bfloat16]
+    NUM_TOKENS = [7, 83, 4096]
+    HIDDEN_SIZES = [768, 769, 770, 771, 5120, 5124, 5125, 5126, 8192, 8199]
+    ADD_RESIDUAL = [False, True]
+    SEEDS = [0]
+
+    @classmethod
+    def setUpClass(cls):
+        if not torch.cuda.is_available():
+            raise unittest.SkipTest("CUDA is not available")
+        torch.set_default_device("cuda")
+
+    def _run_rms_norm_test(self, num_tokens, hidden_size, add_residual, dtype, seed):
+        torch.manual_seed(seed)
+
+        layer = RMSNorm(hidden_size).to(dtype=dtype)
+        layer.weight.data.normal_(mean=1.0, std=0.1)
+        scale = 1 / (2 * hidden_size)
+        x = torch.randn(num_tokens, hidden_size, dtype=dtype) * scale
+        residual = torch.randn_like(x) * scale if add_residual else None
+
+        with torch.inference_mode():
+            ref_out = layer.forward_native(x, residual)
+            out = layer(x, residual)
+
+        if add_residual:
+            self.assertTrue(torch.allclose(out[0], ref_out[0], atol=1e-2, rtol=1e-2))
+            self.assertTrue(torch.allclose(out[1], ref_out[1], atol=1e-2, rtol=1e-2))
+        else:
+            self.assertTrue(torch.allclose(out, ref_out, atol=1e-2, rtol=1e-2))
+
+    def test_rms_norm(self):
+        for params in itertools.product(
+            self.NUM_TOKENS,
+            self.HIDDEN_SIZES,
+            self.ADD_RESIDUAL,
+            self.DTYPES,
+            self.SEEDS,
+        ):
+            with self.subTest(
+                num_tokens=params[0],
+                hidden_size=params[1],
+                add_residual=params[2],
+                dtype=params[3],
+                seed=params[4],
+            ):
+                self._run_rms_norm_test(*params)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)