sglang0.4.5.post1/sgl-kernel/csrc/speculative/speculative_sampling.cu

/*
 * Copyright (c) 2025 by SGLang team.
 * Copyright (c) 2025 by FlashInfer 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.
 */
#include "pytorch_extension_utils.h"
#include "speculative_sampling.cuh"

using namespace flashinfer;

// predicts: [tot_num_draft_tokens]
// accept_index: [bs, num_spec_step]
// accept_token_num: [bs]
// candidates: [bs, num_draft_tokens]
// retrive_index: [bs, num_draft_tokens]
// retrive_next_token: [bs, num_draft_tokens]
// retrive_next_sibling: [bs, num_draft_tokens]
// uniform_samples: [bs, num_draft_tokens]
// target_probs: [bs, num_draft_tokens, vocab_size]
void tree_speculative_sampling_target_only(
    at::Tensor predicts,
    at::Tensor accept_index,
    at::Tensor accept_token_num,  // mutable
    at::Tensor candidates,
    at::Tensor retrive_index,
    at::Tensor retrive_next_token,
    at::Tensor retrive_next_sibling,
    at::Tensor uniform_samples,
    at::Tensor target_probs,
    at::Tensor draft_probs,
    double threshold_single,
    double threshold_acc,
    bool deterministic = true,
    int64_t cuda_stream = 0) {
  CHECK_INPUT(candidates);
  CHECK_INPUT(retrive_index);
  CHECK_INPUT(retrive_next_token);
  CHECK_INPUT(retrive_next_sibling);
  CHECK_INPUT(uniform_samples);
  CHECK_INPUT(target_probs);
  auto device = target_probs.device();
  CHECK_EQ(candidates.device(), device);
  CHECK_EQ(retrive_index.device(), device);
  CHECK_EQ(retrive_next_token.device(), device);
  CHECK_EQ(retrive_next_sibling.device(), device);
  CHECK_EQ(uniform_samples.device(), device);
  CHECK_EQ(target_probs.device(), device);
  CHECK_DIM(1, predicts);
  CHECK_DIM(2, accept_index);
  CHECK_DIM(1, accept_token_num);
  CHECK_DIM(2, candidates);
  CHECK_DIM(2, retrive_index);
  CHECK_DIM(2, retrive_next_token);
  CHECK_DIM(2, retrive_next_sibling);
  CHECK_DIM(2, uniform_samples);
  CHECK_DIM(3, target_probs);
  CHECK_DIM(3, draft_probs);
  unsigned int batch_size = uniform_samples.size(0);
  unsigned int num_spec_step = accept_index.size(1);
  unsigned int num_draft_tokens = candidates.size(1);
  unsigned int vocab_size = target_probs.size(2);
  CHECK_EQ(batch_size, candidates.size(0));
  CHECK_EQ(batch_size, retrive_index.size(0));
  CHECK_EQ(batch_size, retrive_next_token.size(0));
  CHECK_EQ(batch_size, retrive_next_sibling.size(0));
  CHECK_EQ(batch_size, target_probs.size(0));
  CHECK_EQ(num_draft_tokens, retrive_index.size(1));
  CHECK_EQ(num_draft_tokens, retrive_next_token.size(1));
  CHECK_EQ(num_draft_tokens, retrive_next_sibling.size(1));
  CHECK_EQ(num_draft_tokens, uniform_samples.size(1));
  CHECK_EQ(num_draft_tokens, target_probs.size(1));
  CHECK_EQ(vocab_size, target_probs.size(2));
  CHECK_EQ(batch_size, accept_index.size(0));
  CHECK_EQ(batch_size, accept_token_num.size(0));
  if (predicts.scalar_type() != at::kInt) {
    throw std::runtime_error("Expected 'predicts' to be of type int (torch.int32).");
  }
  if (accept_index.scalar_type() != at::kInt) {
    throw std::runtime_error("Expected 'accept_index' to be of type int (torch.int32).");
  }
  if (accept_token_num.scalar_type() != at::kInt) {
    throw std::runtime_error("Expected 'accept_token_num' to be of type int (torch.int32).");
  }
  if (candidates.scalar_type() != at::kInt) {
    throw std::runtime_error("Expected 'candidates' to be of type int (torch.int32).");
  }
  if (retrive_index.scalar_type() != at::kInt) {
    throw std::runtime_error("Expected 'retrive_index' to be of type int (torch.int32).");
  }
  if (retrive_next_token.scalar_type() != at::kInt) {
    throw std::runtime_error("Expected 'retrive_next_token' to be of type int (torch.int32).");
  }
  if (retrive_next_sibling.scalar_type() != at::kInt) {
    throw std::runtime_error("Expected 'retrive_next_sibling' to be of type int (torch.int32).");
  }
  if (uniform_samples.scalar_type() != at::kFloat) {
    throw std::runtime_error("Expected 'uniform_samples' to be of type float (torch.float32).");
  }
  if (target_probs.scalar_type() != at::kFloat) {
    throw std::runtime_error("Expected 'target_probs' to be of type float (torch.float32).");
  }
  if (draft_probs.scalar_type() != at::kFloat) {
    throw std::runtime_error("Expected 'target_probs' to be of type float (torch.float32).");
  }
  CHECK_GE(threshold_single, 0);
  CHECK_GE(1, threshold_single);
  CHECK_GE(threshold_acc, 0);
  CHECK_GE(1, threshold_acc);

  cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream);
  cudaError_t status = sampling::TreeSpeculativeSamplingTargetOnly<float, int>(
      static_cast<int*>(predicts.data_ptr()),
      static_cast<int*>(accept_index.data_ptr()),
      static_cast<int*>(accept_token_num.data_ptr()),
      static_cast<int*>(candidates.data_ptr()),
      static_cast<int*>(retrive_index.data_ptr()),
      static_cast<int*>(retrive_next_token.data_ptr()),
      static_cast<int*>(retrive_next_sibling.data_ptr()),
      static_cast<float*>(uniform_samples.data_ptr()),
      static_cast<float*>(target_probs.data_ptr()),
      static_cast<float*>(draft_probs.data_ptr()),
      batch_size,
      num_spec_step,
      num_draft_tokens,
      vocab_size,
      static_cast<float>(threshold_single),
      static_cast<float>(threshold_acc),
      deterministic,
      stream);

  TORCH_CHECK(
      status == cudaSuccess,
      "TreeSpeculativeSamplingTargetOnly failed with error code " + std::string(cudaGetErrorString(status)));
}