[Feature][Optimization] Qwen Support Dynamic block_wise_fp8 cache (#5486)

Author: ckl117

custom_ops/gpu_ops/append_attn/decoder_write_cache_with_rope_impl.cuh

@@ -849,6 +849,315 @@ __global__ void append_decode_cache_T_quant_neox_rope_kernel(
 #endif
 }
 
+template <typename T,
+          int VecSize = 4,
+          int RoundType = 0,
+          int HeadDim = 128,
+          bool is_scale_channel_wise = false,
+          bool IsFP8 = true,
+          bool IsDynamic = true>
+__global__ void append_decode_cache_T_int8_neox_rope_kernel(
+    const T* __restrict__ quant_qkv,    // [bsz, num_heads + 2 * kv_num_heads,
+                                        // head_size]
+    uint8_t* __restrict__ key_cache,    // [num_blocks, kv_num_heads,
+                                        // block_size, head_size // 2]
+    uint8_t* __restrict__ value_cache,  // [num_blocks, kv_num_heads,
+                                        // block_size, head_size // 2]
+    T* __restrict__ qkv_out,
+    const int* __restrict__ block_tables,  // [bsz, max_blocks_per_seq]
+    const int* __restrict__ cu_seqlens_q,
+    const int* __restrict__ seq_lens,          // [bsz]
+    const int* __restrict__ seq_lens_encoder,  // [bsz]
+    const float* __restrict__ cos_emb,
+    const float* __restrict__ sin_emb,
+    T* __restrict__ cache_k_scale,
+    T* __restrict__ cache_v_scale,
+    const int max_seq_len,
+    const int max_blocks_per_seq,
+    const int num_heads,
+    const int block_size,
+    const float max_bound,
+    const float min_bound,
+    const int kv_num_heads,
+    const bool rope_3d,
+    const float rms_norm_eps) {
+  static_assert(HeadDim == 128, "just support HeadDim be 128 now!");
+  static_assert(VecSize == 4, "just support VecSize be 4 now, 32 * 4!");
+  constexpr int NUM_WARPS = 4;
+  const int tid = threadIdx.x;
+  const int wid = tid / 32;
+  const int lane_id = tid % 32;
+  const int bid = blockIdx.x, head_idx = blockIdx.y * NUM_WARPS + wid;
+  int q_head_idx, k_head_idx, v_idx;
+  const int64_t hidden_size = (num_heads + 2 * kv_num_heads) * HeadDim;
+  constexpr int half_head_size = HeadDim / 2;
+  const int start_token_idx = cu_seqlens_q[bid];
+  if (seq_lens_encoder[bid] > 0) return;
+  const int write_seq_id = seq_lens[bid];
+  if (write_seq_id == 0) return;
+  const int* block_table_now = nullptr;
+
+  block_table_now = block_tables + bid * max_blocks_per_seq;
+  const int block_idx = __ldg(&block_table_now[write_seq_id / block_size]);
+  const int block_offset = write_seq_id % block_size;
+
+  float thread_m2 = 0.0f;
+  float warp_m2 = 0.0f;
+#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
+  cudaGridDependencySynchronize();
+#endif
+  if (head_idx < num_heads) {
+    // q
+    using LoadT = AlignedVector<T, VecSize>;
+    using LoadBiasT = AlignedVector<T, VecSize>;
+    constexpr int HalfVecSize = VecSize / 2;
+    using LoadEmbT = AlignedVector<float, VecSize>;
+
+    LoadT src_vec;
+    LoadT src_vec_right;
+    LoadBiasT out_vec;
+    LoadBiasT out_vec_right;
+    LoadEmbT cos_emb_vec;
+    LoadEmbT sin_emb_vec;
+    const T* qkv_now = quant_qkv + start_token_idx * hidden_size;
+    T* qkv_out_now = qkv_out + start_token_idx * hidden_size;
+#pragma unroll
+    for (uint32_t head_bias = lane_id * VecSize; head_bias < half_head_size;
+         head_bias += 32 * VecSize) {
+      const int bias_idx = head_idx * HeadDim + head_bias;
+      Load<T, VecSize>(&qkv_now[bias_idx], &src_vec);
+      Load<T, VecSize>(&qkv_now[bias_idx + half_head_size], &src_vec_right);
+      // q rope
+      const uint32_t emb_idx = write_seq_id * HeadDim + head_bias;
+      const uint32_t new_emb_idx =
+          rope_3d ? emb_idx + bid * max_seq_len * HeadDim : emb_idx;
+      Load<float, VecSize>(&cos_emb[new_emb_idx], &cos_emb_vec);
+      Load<float, VecSize>(&sin_emb[new_emb_idx], &sin_emb_vec);
+#pragma unroll
+      for (int i = 0; i < VecSize; i++) {
+        // dequant + add_bias + rope
+        float input_left = static_cast<float>(src_vec[i]);
+        float input_right = static_cast<float>(src_vec_right[i]);
+
+        const float cos_tmp = cos_emb_vec[i];
+        const float sin_tmp = sin_emb_vec[i];
+        float tmp1 = input_left * cos_tmp - input_right * sin_tmp;
+        float tmp2 = input_right * cos_tmp + input_left * sin_tmp;
+        thread_m2 += tmp1 * tmp1 + tmp2 * tmp2;
+        out_vec[i] = static_cast<T>(tmp1);
+        out_vec_right[i] = static_cast<T>(tmp2);
+      }
+      Store<T, VecSize>(out_vec, &qkv_out_now[bias_idx]);
+      Store<T, VecSize>(out_vec_right, &qkv_out_now[bias_idx + half_head_size]);
+    }
+  } else if (head_idx < num_heads + 2 * kv_num_heads) {
+    // k
+    constexpr int KV_VEC_SIZE = 16 / sizeof(uint8_t);  // 16
+    using LoadPadKVT = AlignedVector<uint8_t, KV_VEC_SIZE>;
+    const uint32_t kv_head_idx = (head_idx - num_heads) % kv_num_heads;
+    if (block_offset == 0) {
+      // pad zero for this kv_head_idx for this block
+      LoadPadKVT pad_cache_vec;
+      *(reinterpret_cast<uint4*>(pad_cache_vec.val)) = make_uint4(0, 0, 0, 0);
+      if (head_idx < num_heads + kv_num_heads) {
+        constexpr int num_vecs_per_head_dim = HeadDim / KV_VEC_SIZE;
+        constexpr int num_token_each_time = 32 / num_vecs_per_head_dim;
+        const uint32_t tgt_idx =
+            (block_idx * kv_num_heads + kv_head_idx) * block_size * HeadDim +
+            lane_id % num_vecs_per_head_dim * KV_VEC_SIZE;
+        for (int block_i = lane_id / num_vecs_per_head_dim;
+             block_i < block_size;
+             block_i += num_token_each_time) {
+          Store<uint8_t, KV_VEC_SIZE>(pad_cache_vec,
+                                      &key_cache[tgt_idx + block_i * HeadDim]);
+        }
+      } else {
+        const int num_vecs_per_head_dim = block_size / KV_VEC_SIZE;
+        const int num_token_each_time = 32 / num_vecs_per_head_dim;
+        const uint32_t tgt_idx =
+            (block_idx * kv_num_heads + kv_head_idx) * HeadDim * block_size +
+            lane_id % num_vecs_per_head_dim * KV_VEC_SIZE;
+        for (int block_i = lane_id / num_vecs_per_head_dim; block_i < HeadDim;
+             block_i += num_token_each_time) {
+          Store<uint8_t, KV_VEC_SIZE>(
+              pad_cache_vec, &value_cache[tgt_idx + block_i * block_size]);
+        }
+      }
+      __syncwarp();
+    }
+
+    constexpr int K_VEC_SIZE = 4;
+    constexpr int HALF_K_VEC_SIZE = 2;
+    using LoadKVResT = AlignedVector<uint8_t, K_VEC_SIZE>;
+    using LoadKVT = AlignedVector<uint8_t, HALF_K_VEC_SIZE>;
+    using LoadT = AlignedVector<T, HALF_K_VEC_SIZE>;
+    using LoadBiasT = AlignedVector<T, HALF_K_VEC_SIZE>;
+    using LoadEmbT = AlignedVector<float, HALF_K_VEC_SIZE>;
+    LoadKVResT cache_vec;
+    LoadT src_vec1, src_vec1_right, src_vec2, src_vec2_right;
+    LoadBiasT out_vec1, out_vec2;
+    LoadEmbT cos_emb_vec1, cos_emb_vec2;
+    LoadEmbT sin_emb_vec1, sin_emb_vec2;
+
+    const T* qkv_now = quant_qkv + start_token_idx * hidden_size;
+    const int head_bias = lane_id / 4 * 16 + lane_id % 4 * 2;
+    const int bias_idx = head_idx * HeadDim + head_bias;
+    Load<T, HALF_K_VEC_SIZE>(&qkv_now[bias_idx], &src_vec1);
+    Load<T, HALF_K_VEC_SIZE>(&qkv_now[bias_idx + 8], &src_vec2);
+    T scale = T(1.0f);
+    const int k_head_idx = head_idx - num_heads;
+    const int v_head_idx = head_idx - num_heads - kv_num_heads;
+    if (head_idx < num_heads + kv_num_heads) {
+      Load<T, HALF_K_VEC_SIZE>(
+          &qkv_now[head_idx * HeadDim + (head_bias + half_head_size) % HeadDim],
+          &src_vec1_right);
+      Load<T, HALF_K_VEC_SIZE>(
+          &qkv_now[head_idx * HeadDim +
+                   (head_bias + 8 + half_head_size) % HeadDim],
+          &src_vec2_right);
+
+      const uint32_t emb_idx = write_seq_id * HeadDim + head_bias;
+      const uint32_t new_emb_idx =
+          rope_3d ? emb_idx + bid * max_seq_len * HeadDim : emb_idx;
+      Load<float, HALF_K_VEC_SIZE>(&cos_emb[new_emb_idx], &cos_emb_vec1);
+      Load<float, HALF_K_VEC_SIZE>(&cos_emb[new_emb_idx + 8], &cos_emb_vec2);
+      Load<float, HALF_K_VEC_SIZE>(&sin_emb[new_emb_idx], &sin_emb_vec1);
+      Load<float, HALF_K_VEC_SIZE>(&sin_emb[new_emb_idx + 8], &sin_emb_vec2);
+    }
+
+    if (head_idx < num_heads + kv_num_heads) {
+      float input_left = static_cast<float>(src_vec1[0]);
+      float input_right = static_cast<float>(src_vec1_right[0]);
+      float cos_tmp = cos_emb_vec1[0];
+      float sin_tmp = sin_emb_vec1[0];
+      float tmp1 = 0;
+      if (head_bias < half_head_size) {
+        tmp1 = input_left * cos_tmp - input_right * sin_tmp;
+      } else {
+        tmp1 = input_left * cos_tmp + input_right * sin_tmp;
+      }
+      out_vec1[0] = static_cast<T>(tmp1);
+      input_left = static_cast<float>(src_vec1[1]);
+      input_right = static_cast<float>(src_vec1_right[1]);
+      cos_tmp = cos_emb_vec1[1];
+      sin_tmp = sin_emb_vec1[1];
+      if (head_bias < half_head_size) {
+        tmp1 = input_left * cos_tmp - input_right * sin_tmp;
+      } else {
+        tmp1 = input_left * cos_tmp + input_right * sin_tmp;
+      }
+      out_vec1[1] = static_cast<T>(tmp1);
+    } else {
+      out_vec1[0] = src_vec1[0];
+      out_vec1[1] = src_vec1[1];
+    }
+
+    // rope
+    if (head_idx < num_heads + kv_num_heads) {
+      float input_left = static_cast<float>(src_vec2[0]);
+      float input_right = static_cast<float>(src_vec2_right[0]);
+      float cos_tmp = cos_emb_vec2[0];
+      float sin_tmp = sin_emb_vec2[0];
+      float tmp1 = 0;
+      if (head_bias < half_head_size) {
+        tmp1 = input_left * cos_tmp - input_right * sin_tmp;
+      } else {
+        tmp1 = input_left * cos_tmp + input_right * sin_tmp;
+      }
+      out_vec2[0] = static_cast<T>(tmp1);
+      input_left = static_cast<float>(src_vec2[1]);
+      input_right = static_cast<float>(src_vec2_right[1]);
+      cos_tmp = cos_emb_vec2[1];
+      sin_tmp = sin_emb_vec2[1];
+      if (head_bias < half_head_size) {
+        tmp1 = input_left * cos_tmp - input_right * sin_tmp;
+      } else {
+        tmp1 = input_left * cos_tmp + input_right * sin_tmp;
+      }
+      out_vec2[1] = static_cast<T>(tmp1);
+    } else {
+      out_vec2[0] = src_vec2[0];
+      out_vec2[1] = src_vec2[1];
+    }
+    if constexpr (IsDynamic) {
+      // reduce max, 1 head per warp
+      T local_max = -INFINITY;
+#pragma unroll
+      for (int i = 0; i < HALF_K_VEC_SIZE; i++) {
+        local_max = __hmax(local_max, __habs(out_vec1[i]));
+        local_max = __hmax(local_max, __habs(out_vec2[i]));
+      }
+#pragma unroll
+      for (int m_offset = 16; m_offset > 0; m_offset /= 2) {
+        local_max =
+            __hmax(local_max, __shfl_xor_sync(0xffffffff, local_max, m_offset));
+      }
+      scale = __hdiv(448, local_max);
+
+      int cache_offset;
+      if (head_idx < num_heads) {
+        cache_offset = 0;
+      } else if (head_idx < num_heads + 2 * kv_num_heads) {
+        cache_offset = block_idx * kv_num_heads * block_size +
+                       (head_idx - num_heads) % kv_num_heads * block_size +
+                       block_offset;
+      }
+      T* cache_k_scale_now = cache_k_scale + cache_offset;
+      T* cache_v_scale_now = cache_v_scale + cache_offset;
+      if (lane_id == 0) {
+        if (head_idx < num_heads + kv_num_heads) {
+          cache_k_scale_now[0] = __hdiv(1, scale);
+        } else {
+          cache_v_scale_now[0] = __hdiv(1, scale);
+        }
+      }
+    } else {
+      if (head_idx < num_heads + kv_num_heads) {
+        scale = __ldg(&cache_k_scale[kv_head_idx]);
+      } else {
+        scale = __ldg(&cache_v_scale[kv_head_idx]);
+      }
+    }
+
+#pragma unroll
+    for (uint32_t i = 0; i < HALF_K_VEC_SIZE; i++) {
+      cache_vec[i] = QuantToC8<T, true, IsFP8, RoundType>(
+          scale, out_vec1[i], max_bound, min_bound);
+      cache_vec[i + HALF_K_VEC_SIZE] = QuantToC8<T, true, IsFP8, RoundType>(
+          scale, out_vec2[i], max_bound, min_bound);
+    }
+    if (head_idx < num_heads + kv_num_heads) {
+      const int start_block_16 =
+          block_offset / 16 * 16 + block_offset % 8 + lane_id / 4 % 2 * 8;
+      const uint32_t tgt_cache_idx =
+          block_idx * kv_num_heads * block_size * HeadDim +
+          kv_head_idx * block_size * HeadDim + start_block_16 * HeadDim +
+          lane_id / 4 / 2 * 32 + (block_offset % 16) / 8 * 16 + lane_id % 4 * 4;
+      Store<uint8_t, K_VEC_SIZE>(cache_vec, &key_cache[tgt_cache_idx]);
+    } else {
+      const uint32_t base_tgt_cache_idx =
+          block_idx * kv_num_heads * HeadDim * block_size +
+          kv_head_idx * HeadDim * block_size +
+          (lane_id / 4 * 16 + lane_id % 4 * 2) * block_size +
+          block_offset / 16 % 2 * 8 * block_size + block_offset / 16 / 2 * 32;
+      const uint32_t tgt_cache_idx1 = base_tgt_cache_idx +
+                                      block_offset % 8 / 2 * 4     // per 4
+                                      + block_offset % 16 / 8 * 2  // per 2
+                                      + block_offset % 2;          // per 1
+      const uint32_t tgt_cache_idx2 = tgt_cache_idx1 + block_size;
+      const uint32_t tgt_cache_idx3 = tgt_cache_idx1 + 16;
+      const uint32_t tgt_cache_idx4 = tgt_cache_idx3 + block_size;
+      value_cache[tgt_cache_idx1] = cache_vec[0];
+      value_cache[tgt_cache_idx2] = cache_vec[1];
+      value_cache[tgt_cache_idx3] = cache_vec[2];
+      value_cache[tgt_cache_idx4] = cache_vec[3];
+    }
+  }
+#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900))
+  cudaTriggerProgrammaticLaunchCompletion();
+#endif
+}
+
 template <typename T,
           int VecSize = 4,
           int RoundType = 0,