mirror of
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401 lines
16 KiB
Plaintext
401 lines
16 KiB
Plaintext
// Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "helper.h"
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#include "iluvatar_context.h"
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template <paddle::DataType T>
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void PrefillFusedPagedAttnKernel(
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const paddle::Tensor& qkv,
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paddle::Tensor& k_cache,
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paddle::Tensor& v_cache,
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const paddle::Tensor& block_table,
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const paddle::Tensor& cu_seqlens_qkv,
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const paddle::optional<paddle::Tensor>& rope_sin,
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const paddle::optional<paddle::Tensor>& rope_cos,
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int num_heads,
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int head_dim,
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int num_kv_heads,
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int block_size,
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int max_seq_len,
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float scale,
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bool causal,
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bool q_rope,
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bool k_rope,
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bool v_rope,
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paddle::Tensor& out) {
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// check dtype and contiguous
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const auto& dtype = qkv.dtype();
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cuinferDataType_t data_type;
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if (dtype == paddle::DataType::FLOAT16) {
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data_type = CUINFER_DATA_HALF;
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} else if (dtype == paddle::DataType::BFLOAT16) {
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data_type = CUINFER_DATA_BFLOAT16;
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} else {
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common::errors::InvalidArgument(
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"paged_attention support half and bfloat16 now");
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}
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PADDLE_ENFORCE_EQ(k_cache.dtype(),
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dtype,
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common::errors::InvalidArgument(
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"k_cache dtype must be the same as query dtype"));
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PADDLE_ENFORCE_EQ(k_cache.is_contiguous(),
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true,
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common::errors::InvalidArgument(
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"paged_attention expects k_cache is contiguous"));
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PADDLE_ENFORCE_EQ(
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block_table.dtype(),
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paddle::DataType::INT32,
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common::errors::InvalidArgument("block_table dtype must be int32"));
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PADDLE_ENFORCE_EQ(block_table.is_contiguous(),
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true,
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common::errors::InvalidArgument(
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"paged_attention expects block_table is contiguous"));
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PADDLE_ENFORCE_EQ(
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cu_seqlens_qkv.dtype(),
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paddle::DataType::INT32,
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common::errors::InvalidArgument("cu_seqlens_qkv dtype must be int32"));
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PADDLE_ENFORCE_EQ(
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cu_seqlens_qkv.is_contiguous(),
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true,
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common::errors::InvalidArgument(
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"paged_attention expects cu_seqlens_qkv is contiguous"));
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// check dim and shape
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// k_cache: [num_blocks, kv_num_heads, block_size, head_dim]
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// v_cache: [num_blocks, kv_num_heads, block_size, head_dim]
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// block_table: [batch_size, max_num_blocks_per_seq]
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// seq_lens: [batch_size]
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// qkv: [num_tokens, (num_heads+2*num_kv_heads)*head_dim]
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// out: [num_tokens, hidden_size]
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const auto& qkv_dims = qkv.dims();
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PADDLE_ENFORCE_EQ(qkv_dims.size(),
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2,
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common::errors::InvalidArgument(
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"paged_attn receive query dims is "
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"[num_tokens, (num_heads+2*num_kv_heads)*head_dim]"));
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PADDLE_ENFORCE_EQ(
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out.dims().size(),
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2,
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common::errors::InvalidArgument("paged_attn receive out dims is "
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"[num_tokens, hidden_size]"));
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const auto& kv_cache_dims = k_cache.dims();
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PADDLE_ENFORCE_EQ(kv_cache_dims.size(),
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4,
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common::errors::InvalidArgument(
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"paged_attn receive kv cache dims is "
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"[num_blocks, kv_num_heads, block_size, head_dim]"));
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const auto& block_table_dims = block_table.dims();
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PADDLE_ENFORCE_EQ(
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block_table_dims.size(),
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2,
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common::errors::InvalidArgument("paged_attn receive block_table dims is "
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"[batch_size, max_num_blocks_per_seq]"));
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const auto& cu_seqlens_qkv_dims = cu_seqlens_qkv.dims();
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PADDLE_ENFORCE_EQ(
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cu_seqlens_qkv_dims.size(),
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1,
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common::errors::InvalidArgument(
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"paged_attn receive cu_seqlens_qkv dims is [batch_size]"));
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int batch_size = block_table_dims[0];
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int num_tokens = qkv_dims[0];
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int num_total_heads = num_heads + 2 * num_kv_heads;
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int qkv_stride = qkv.strides()[0];
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int num_blocks = kv_cache_dims[0];
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PADDLE_ENFORCE_EQ(kv_cache_dims[1],
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num_kv_heads,
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common::errors::InvalidArgument(
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"kv_cache_dims[1] must be equal to num_kv_head"));
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PADDLE_ENFORCE_EQ(kv_cache_dims[2],
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block_size,
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common::errors::InvalidArgument(
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"kv_cache_dims[2] must be equal to block_size"));
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PADDLE_ENFORCE_EQ(kv_cache_dims[3],
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head_dim,
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common::errors::InvalidArgument(
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"kv_cache_dims[3] must be equal to head_dim"));
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PADDLE_ENFORCE_EQ(
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cu_seqlens_qkv_dims[0],
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batch_size + 1,
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common::errors::InvalidArgument(
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"cu_seqlens_qkv_dims[0] must be equal to batch_size + 1"));
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int block_table_stride = block_table.strides()[0];
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const float* rope_sin_ptr = rope_sin ? rope_sin.get().data<float>() : nullptr;
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const float* rope_cos_ptr = rope_cos ? rope_cos.get().data<float>() : nullptr;
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cuinferHandle_t cuinfer_handle =
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iluvatar::getContextInstance()->getIxInferHandle();
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size_t workspace_size = 0;
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CUINFER_CHECK(cuinferGetFmhaFwdMergedFuseRopeWorkspaceSize(num_tokens,
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num_heads,
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num_kv_heads,
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head_dim,
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q_rope,
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k_rope,
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v_rope,
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data_type,
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data_type,
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data_type,
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&workspace_size));
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auto* allocator = paddle::GetAllocator(qkv.place());
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phi::Allocator::AllocationPtr tmp_workspace =
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allocator->Allocate(workspace_size);
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void* workspace_ptr = tmp_workspace->ptr();
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cuinferTensorDescriptor_t qkv_desc;
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CUINFER_CHECK(cuinferCreateTensorDescriptor(&qkv_desc));
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CUINFER_CHECK(cuinferSetTensorNdDescriptor(
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qkv_desc,
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data_type,
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3,
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std::vector<int>({num_tokens, num_total_heads, head_dim}).data(),
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std::vector<int>({num_total_heads * head_dim, head_dim, 1}).data()));
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cuinferTensorDescriptor_t qkv_seqlens_desc;
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CUINFER_CHECK(cuinferCreateTensorDescriptor(&qkv_seqlens_desc));
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CUINFER_CHECK(
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cuinferSetTensorNdDescriptor(qkv_seqlens_desc,
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CUINFER_DATA_INT32,
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1,
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std::vector<int>({batch_size + 1}).data(),
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std::vector<int>({1}).data()));
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cuinferTensorDescriptor_t block_table_desc;
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CUINFER_CHECK(cuinferCreateTensorDescriptor(&block_table_desc));
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CUINFER_CHECK(cuinferSetTensorNdDescriptor(
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block_table_desc,
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CUINFER_DATA_INT32,
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2,
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std::vector<int>({batch_size, block_table_stride}).data(),
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std::vector<int>({block_table_stride, 1}).data()));
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cuinferTensorDescriptor_t o_desc;
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CUINFER_CHECK(cuinferCreateTensorDescriptor(&o_desc));
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CUINFER_CHECK(cuinferSetTensorNdDescriptor(
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o_desc,
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data_type,
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3,
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std::vector<int>({num_tokens, num_heads, head_dim}).data(),
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std::vector<int>({num_heads * head_dim, head_dim, 1}).data()));
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cuinferTensorDescriptor_t k_cache_desc;
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CUINFER_CHECK(cuinferCreateTensorDescriptor(&k_cache_desc));
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CUINFER_CHECK(cuinferSetTensorNdDescriptor(
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k_cache_desc,
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data_type,
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4,
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std::vector<int>({num_blocks, num_kv_heads, block_size, head_dim}).data(),
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std::vector<int>({num_kv_heads * block_size * head_dim,
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block_size * head_dim,
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head_dim,
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1})
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.data()));
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cuinferTensorDescriptor_t v_cache_desc;
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CUINFER_CHECK(cuinferCreateTensorDescriptor(&v_cache_desc));
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CUINFER_CHECK(cuinferSetTensorNdDescriptor(
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v_cache_desc,
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data_type,
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4,
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std::vector<int>({num_blocks, num_kv_heads, block_size, head_dim}).data(),
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std::vector<int>({num_kv_heads * block_size * head_dim,
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block_size * head_dim,
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head_dim,
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1})
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.data()));
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cuinferTensorDescriptor_t cos_desc;
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CUINFER_CHECK(cuinferCreateTensorDescriptor(&cos_desc));
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CUINFER_CHECK(cuinferSetTensorNdDescriptor(
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cos_desc,
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CUINFER_DATA_FLOAT,
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2,
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std::vector<int>({max_seq_len, head_dim}).data(),
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std::vector<int>({head_dim, 1}).data()));
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cuinferTensorDescriptor_t sin_desc;
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CUINFER_CHECK(cuinferCreateTensorDescriptor(&sin_desc));
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CUINFER_CHECK(cuinferSetTensorNdDescriptor(
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sin_desc,
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CUINFER_DATA_FLOAT,
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2,
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std::vector<int>({max_seq_len, head_dim}).data(),
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std::vector<int>({head_dim, 1}).data()));
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CUINFER_CHECK(cuinferFmhaFwdMergedFuseRopeFunc(cuinfer_handle,
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qkv_desc,
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qkv.data(),
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qkv_seqlens_desc,
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cu_seqlens_qkv.data<int32_t>(),
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block_table_desc,
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block_table.data<int32_t>(),
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o_desc,
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out.data(),
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k_cache_desc,
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k_cache.data(),
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v_cache_desc,
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v_cache.data(),
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workspace_ptr,
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workspace_size,
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cos_desc,
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rope_cos_ptr,
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sin_desc,
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rope_sin_ptr,
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batch_size,
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num_heads,
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num_kv_heads,
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head_dim,
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causal,
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scale,
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q_rope,
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k_rope,
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v_rope));
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CUINFER_CHECK(cuinferDestroyTensorDescriptor(qkv_desc));
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CUINFER_CHECK(cuinferDestroyTensorDescriptor(qkv_seqlens_desc));
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CUINFER_CHECK(cuinferDestroyTensorDescriptor(block_table_desc));
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CUINFER_CHECK(cuinferDestroyTensorDescriptor(o_desc));
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CUINFER_CHECK(cuinferDestroyTensorDescriptor(k_cache_desc));
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CUINFER_CHECK(cuinferDestroyTensorDescriptor(v_cache_desc));
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CUINFER_CHECK(cuinferDestroyTensorDescriptor(cos_desc));
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CUINFER_CHECK(cuinferDestroyTensorDescriptor(sin_desc));
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}
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std::vector<paddle::Tensor> PrefillFusedPagedAttn(
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const paddle::Tensor& qkv,
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paddle::Tensor& k_cache,
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paddle::Tensor& v_cache,
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const paddle::Tensor& block_table,
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const paddle::Tensor& cu_seqlens_qkv,
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const paddle::optional<paddle::Tensor>& rope_sin,
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const paddle::optional<paddle::Tensor>& rope_cos,
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int num_heads,
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int head_dim,
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int num_kv_heads,
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int block_size,
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int max_seq_len,
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float scale,
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bool causal,
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bool q_rope,
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bool k_rope,
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bool v_rope) {
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const auto dtype = qkv.dtype();
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auto out =
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paddle::empty({qkv.shape()[0], num_heads * head_dim}, dtype, qkv.place());
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switch (dtype) {
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case paddle::DataType::BFLOAT16:
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PrefillFusedPagedAttnKernel<paddle::DataType::BFLOAT16>(qkv,
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k_cache,
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v_cache,
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block_table,
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cu_seqlens_qkv,
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rope_sin,
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rope_cos,
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num_heads,
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head_dim,
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num_kv_heads,
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block_size,
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max_seq_len,
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scale,
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causal,
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q_rope,
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k_rope,
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v_rope,
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out);
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break;
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case paddle::DataType::FLOAT16:
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PrefillFusedPagedAttnKernel<paddle::DataType::FLOAT16>(qkv,
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k_cache,
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v_cache,
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block_table,
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cu_seqlens_qkv,
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rope_sin,
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rope_cos,
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num_heads,
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head_dim,
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num_kv_heads,
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block_size,
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max_seq_len,
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scale,
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causal,
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q_rope,
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k_rope,
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v_rope,
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out);
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break;
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default:
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PD_THROW("Unsupported data type for Paged attn");
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}
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return {out};
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}
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std::vector<std::vector<int64_t>> PrefillFusedPagedAttnInferShape(
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const std::vector<int64_t>& qkv_shape,
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const std::vector<int64_t>& k_cache_shape,
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const std::vector<int64_t>& v_cache_shape,
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const std::vector<int64_t>& block_table_shape,
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const std::vector<int64_t>& cu_seqlens_qkv_shape,
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const std::vector<int64_t>& rope_sin_shape,
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const std::vector<int64_t>& rope_cos_shape,
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int num_heads,
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int head_dim,
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int num_kv_heads,
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int block_size,
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int max_seq_len,
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float scale,
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bool causal,
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bool q_rope,
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bool k_rope,
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bool v_rope) {
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return {{qkv_shape[0], num_heads * head_dim}};
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}
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std::vector<paddle::DataType> PrefillFusedPagedAttnInferDtype(
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const paddle::DataType& qkv_dtype) {
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return {qkv_dtype};
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}
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PD_BUILD_STATIC_OP(prefill_fused_paged_attn)
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.Inputs({"qkv",
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"k_cache",
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"v_cache",
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"block_table",
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"cu_seqlens_qkv",
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paddle::Optional("rope_sin"),
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paddle::Optional("rope_cos")})
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.Outputs({"out"})
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.Attrs({"num_heads:int",
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"head_dim:int",
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"num_kv_heads:int",
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"block_size:int",
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"max_seq_len:int",
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"scale:float",
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"causal:bool",
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"q_rope:bool",
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"k_rope:bool",
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"v_rope:bool"})
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.SetKernelFn(PD_KERNEL(PrefillFusedPagedAttn))
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.SetInferShapeFn(PD_INFER_SHAPE(PrefillFusedPagedAttnInferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(PrefillFusedPagedAttnInferDtype));
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