sglangv0.5.2 & support Qwen3-Next-80B-A3B-Instruct
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maxiao1
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sgl-kernel/python/sgl_kernel/testing/__init__.py
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sgl-kernel/python/sgl_kernel/testing/__init__.py
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sgl-kernel/python/sgl_kernel/testing/rotary_embedding.py
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sgl-kernel/python/sgl_kernel/testing/rotary_embedding.py
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from typing import Any, Dict, List, Optional, Tuple, Union
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import pytest
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import torch
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from sgl_kernel import FusedSetKVBufferArg, apply_rope_with_cos_sin_cache_inplace
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# vLLM torch native
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def _apply_rotary_emb(
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x: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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is_neox_style: bool,
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) -> torch.Tensor:
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"""
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Args:
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x: [num_tokens, num_heads, head_size]
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cos: [num_tokens, head_size // 2]
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sin: [num_tokens, head_size // 2]
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is_neox_style: Whether to use the Neox-style or GPT-J-style rotary
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positional embeddings.
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"""
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cos = cos.unsqueeze(-2).to(x.dtype)
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sin = sin.unsqueeze(-2).to(x.dtype)
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if is_neox_style:
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x1, x2 = torch.chunk(x, 2, dim=-1)
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else:
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x1 = x[..., ::2]
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x2 = x[..., 1::2]
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o1 = x1 * cos - x2 * sin
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o2 = x2 * cos + x1 * sin
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if is_neox_style:
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return torch.cat((o1, o2), dim=-1)
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else:
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return torch.stack((o1, o2), dim=-1).flatten(-2)
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class RotaryEmbedding(torch.nn.Module):
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# Reference: https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/rotary_embedding.py
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def __init__(
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self,
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head_size: int,
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rotary_dim: int,
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max_position_embeddings: int,
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base: int,
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is_neox_style: bool,
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dtype: torch.dtype,
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) -> None:
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super().__init__()
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self.head_size = head_size
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self.rotary_dim = rotary_dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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self.is_neox_style = is_neox_style
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self.dtype = dtype
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cache = self._compute_cos_sin_cache()
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self.cos_sin_cache: torch.Tensor
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self.register_buffer("cos_sin_cache", cache, persistent=False)
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def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
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inv_freq = 1.0 / (
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base
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** (
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torch.arange(0, self.rotary_dim, 2, dtype=torch.float) / self.rotary_dim
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)
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)
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return inv_freq
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def _compute_cos_sin_cache(self) -> torch.Tensor:
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"""Compute the cos and sin cache."""
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inv_freq = self._compute_inv_freq(self.base)
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t = torch.arange(self.max_position_embeddings, dtype=torch.float)
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freqs = torch.einsum("i,j -> ij", t, inv_freq)
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cos = freqs.cos()
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sin = freqs.sin()
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cache = torch.cat((cos, sin), dim=-1)
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return cache
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def forward_native(
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self,
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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offsets: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""A PyTorch-native implementation of forward()."""
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if offsets is not None:
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positions = positions + offsets
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positions = positions.flatten()
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num_tokens = positions.shape[0]
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cos_sin = self.cos_sin_cache.index_select(0, positions)
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# Modification: float32 is required for the rotary embedding to work correctly
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query = query.to(torch.float32)
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key = key.to(torch.float32)
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cos, sin = cos_sin.chunk(2, dim=-1)
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query_shape = query.shape
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query = query.view(num_tokens, -1, self.head_size)
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query_rot = query[..., : self.rotary_dim]
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query_pass = query[..., self.rotary_dim :]
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query_rot = _apply_rotary_emb(query_rot, cos, sin, self.is_neox_style)
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query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
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key_shape = key.shape
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key = key.view(num_tokens, -1, self.head_size)
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key_rot = key[..., : self.rotary_dim]
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key_pass = key[..., self.rotary_dim :]
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key_rot = _apply_rotary_emb(key_rot, cos, sin, self.is_neox_style)
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key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
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# Modification: convert to the correct dtype
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query = query.to(self.dtype)
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key = key.to(self.dtype)
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return query, key
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class FlashInferRotaryEmbedding(RotaryEmbedding):
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def forward_cuda(
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self,
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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fused_set_kv_buffer_arg: Optional[FusedSetKVBufferArg] = None,
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offsets: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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apply_rope_with_cos_sin_cache_inplace(
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positions=positions,
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query=query,
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key=key,
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fused_set_kv_buffer_arg=fused_set_kv_buffer_arg,
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head_size=self.head_size,
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cos_sin_cache=self.cos_sin_cache,
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is_neox=self.is_neox_style,
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)
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return query, key
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class MHATokenToKVPool:
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KV_POOL_SIZE = 16384
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def __init__(
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self,
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head_num: int,
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head_dim: int,
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):
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self.head_num = head_num
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self.head_dim = head_dim
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self.size = MHATokenToKVPool.KV_POOL_SIZE
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self.page_size = 1
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self.store_dtype = torch.bfloat16
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self.device = "cuda"
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self.layer_num = 1
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self.start_layer = 0
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self._create_buffers()
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def _create_buffers(self):
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self.k_buffer = [
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torch.zeros(
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(self.size + self.page_size, self.head_num, self.head_dim),
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dtype=self.store_dtype,
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device=self.device,
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)
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for _ in range(self.layer_num)
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]
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self.v_buffer = [
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torch.zeros(
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(self.size + self.page_size, self.head_num, self.head_dim),
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dtype=self.store_dtype,
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device=self.device,
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)
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for _ in range(self.layer_num)
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]
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def set_kv_buffer(
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self,
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loc: torch.Tensor,
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cache_k: torch.Tensor,
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cache_v: torch.Tensor,
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):
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layer_id = 0
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self.k_buffer[layer_id - self.start_layer][loc] = cache_k
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self.v_buffer[layer_id - self.start_layer][loc] = cache_v
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def create_inputs(
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head_size: int,
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batch_size: int,
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seq_len: int,
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device,
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dtype: torch.dtype,
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num_q_heads: int,
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num_kv_heads: int,
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):
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pos_ids = torch.arange(seq_len, device=device).repeat(batch_size)
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query = torch.randn(
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batch_size * seq_len, num_q_heads * head_size, dtype=dtype, device=device
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)
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key = torch.randn(
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batch_size * seq_len, num_kv_heads * head_size, dtype=dtype, device=device
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)
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value = torch.randn(
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batch_size * seq_len, num_kv_heads * head_size, dtype=dtype, device=device
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)
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out_cache_loc = torch.randperm(
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MHATokenToKVPool.KV_POOL_SIZE, dtype=torch.int64, device=device
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)[: batch_size * seq_len].clone()
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return dict(
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pos_ids=pos_ids, query=query, key=key, value=value, out_cache_loc=out_cache_loc
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)
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