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404 lines
17 KiB
Plaintext
404 lines
17 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 PagedAttnKernel(const paddle::Tensor& q,
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const paddle::Tensor& k_cache,
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const paddle::Tensor& v_cache,
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const paddle::Tensor& block_table,
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const paddle::Tensor& seq_lens,
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const paddle::optional<paddle::Tensor>& alibi_slopes,
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const paddle::optional<paddle::Tensor>& k,
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const paddle::optional<paddle::Tensor>& v,
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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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float scale,
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int block_size,
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int max_context_len,
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bool causal,
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int window_left,
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int window_right,
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float softcap,
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bool enable_cuda_graph,
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bool use_sqrt_alibi,
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bool merged_qkv,
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int rope_batch_stride,
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bool is_interleaved_rope_mode,
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paddle::Tensor& out) {
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if (alibi_slopes) {
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PADDLE_ENFORCE_EQ(alibi_slopes.get().dtype(),
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paddle::DataType::FLOAT32,
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common::errors::InvalidArgument(
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"paged_attention expects alibi_slopes float tensor"));
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PADDLE_ENFORCE_EQ(
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alibi_slopes.get().is_contiguous(),
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true,
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common::errors::InvalidArgument(
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"paged_attention expects alibi_slopes is contiguous"));
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}
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// check dtype and contiguous
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const auto& dtype = q.dtype();
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cudaDataType_t data_type;
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if (dtype == paddle::DataType::FLOAT16) {
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data_type = CUDA_R_16F;
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} else if (dtype == paddle::DataType::BFLOAT16) {
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data_type = CUDA_R_16BF;
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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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seq_lens.dtype(),
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paddle::DataType::INT32,
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common::errors::InvalidArgument("seq_lens dtype must be int32"));
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PADDLE_ENFORCE_EQ(seq_lens.is_contiguous(),
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true,
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common::errors::InvalidArgument(
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"paged_attention expects seq_lens 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: [num_seqs, max_num_blocks_per_seq]
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// seq_lens: [num_seqs]
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// q and out:
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// if merged_qkv = false:
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// q:[num_seqs, hidden_size]
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// out:[num_seqs, hidden_size]
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// if merged_qkv = true:
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// q: [num_seqs, (num_heads+2*num_kv_heads)*head_dim]
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// out: [num_seqs, hidden_size]
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const auto& q_dims = q.dims();
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PADDLE_ENFORCE_EQ(q_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_seqs, (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_seqs, 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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"[num_seqs, max_num_blocks_per_seq]"));
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const auto& seq_lens_dims = seq_lens.dims();
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PADDLE_ENFORCE_EQ(seq_lens_dims.size(),
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1,
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common::errors::InvalidArgument(
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"paged_attn receive seq_lens dims is [num_seqs]"));
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int num_seqs = q_dims[0];
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int max_num_blocks_per_seq = block_table_dims[1];
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int q_stride = q.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(block_table_dims[0],
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num_seqs,
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common::errors::InvalidArgument(
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"block_table_dims[0] must be equal to num_seqs"));
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PADDLE_ENFORCE_EQ(seq_lens_dims[0],
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num_seqs,
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common::errors::InvalidArgument(
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"seq_lens_dims[0] must be equal to num_seqs"));
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int kv_block_stride = k_cache.strides()[0];
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int kv_head_stride = k_cache.strides()[1];
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const float* alibi_slopes_ptr =
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alibi_slopes ? alibi_slopes.get().data<float>() : nullptr;
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const void* key_ptr = k ? k.get().data() : nullptr;
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const void* value_ptr = v ? v.get().data() : nullptr;
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const float* rope_sin_ptr =
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merged_qkv ? rope_sin.get().data<float>() : nullptr;
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const float* rope_cos_ptr =
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merged_qkv ? 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(cuInferPageAttentionGetWorkspaceV7(num_seqs,
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num_heads,
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num_kv_heads,
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head_dim,
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block_size,
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max_context_len,
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&workspace_size));
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auto* allocator = paddle::GetAllocator(q.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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cuinferAttentionRopeMode_t rope_mode =
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is_interleaved_rope_mode ? CUINFER_ATTEN_NORMAL : CUINFER_ATTEN_OCRV1;
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PageAttentionWithKVCacheArguments args{static_cast<float>(scale),
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1.0,
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1.0,
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static_cast<float>(softcap),
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window_left,
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window_right,
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causal,
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use_sqrt_alibi,
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enable_cuda_graph,
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false,
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alibi_slopes_ptr,
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key_ptr,
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value_ptr,
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workspace_ptr,
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merged_qkv,
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rope_sin_ptr,
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rope_cos_ptr,
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nullptr,
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nullptr,
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nullptr,
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nullptr,
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1,
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0,
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0,
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nullptr,
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static_cast<size_t>(rope_batch_stride),
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rope_mode};
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CUINFER_CHECK(cuInferPageAttentionV7(cuinfer_handle,
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out.data(),
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data_type,
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q.data(),
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data_type,
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num_seqs,
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num_heads,
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num_kv_heads,
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head_dim,
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q_stride,
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kv_block_stride,
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kv_head_stride,
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k_cache.data(),
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data_type,
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v_cache.data(),
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data_type,
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block_size,
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max_num_blocks_per_seq,
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max_context_len,
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block_table.data<int32_t>(),
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seq_lens.data<int32_t>(),
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args));
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}
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std::vector<paddle::Tensor> PagedAttn(
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const paddle::Tensor& q,
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const paddle::Tensor& k_cache,
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const paddle::Tensor& v_cache,
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const paddle::Tensor& block_table,
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const paddle::Tensor& seq_lens,
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const paddle::optional<paddle::Tensor>& alibi_slopes,
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const paddle::optional<paddle::Tensor>& k,
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const paddle::optional<paddle::Tensor>& v,
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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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float scale,
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int block_size,
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int max_context_len,
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bool causal,
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int window_left,
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int window_right,
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float softcap,
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bool enable_cuda_graph,
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bool use_sqrt_alibi,
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bool merged_qkv,
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int rope_batch_stride,
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bool is_interleaved_rope_mode) {
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const auto dtype = q.dtype();
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auto out =
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paddle::empty({q.shape()[0], num_heads * head_dim}, dtype, q.place());
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switch (dtype) {
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case paddle::DataType::BFLOAT16:
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PagedAttnKernel<paddle::DataType::BFLOAT16>(q,
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k_cache,
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v_cache,
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block_table,
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seq_lens,
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alibi_slopes,
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k,
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v,
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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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scale,
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block_size,
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max_context_len,
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causal,
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window_left,
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window_right,
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softcap,
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enable_cuda_graph,
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use_sqrt_alibi,
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merged_qkv,
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rope_batch_stride,
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is_interleaved_rope_mode,
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out);
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break;
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case paddle::DataType::FLOAT16:
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PagedAttnKernel<paddle::DataType::FLOAT16>(q,
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k_cache,
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v_cache,
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block_table,
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seq_lens,
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alibi_slopes,
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k,
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v,
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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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scale,
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block_size,
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max_context_len,
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causal,
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window_left,
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window_right,
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softcap,
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enable_cuda_graph,
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use_sqrt_alibi,
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merged_qkv,
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rope_batch_stride,
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is_interleaved_rope_mode,
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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>> PagedAttnInferShape(
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const std::vector<int64_t>& q_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>& seq_lens_shape,
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const std::vector<int64_t>& alibi_slopes_shape,
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const std::vector<int64_t>& k_shape,
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const std::vector<int64_t>& v_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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float scale,
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int block_size,
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int max_context_len,
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bool causal,
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int window_left,
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int window_right,
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float softcap,
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bool enable_cuda_graph,
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bool use_sqrt_alibi,
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bool merged_qkv) {
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if (merged_qkv) {
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return {{q_shape[0], num_heads * head_dim}};
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} else {
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return {q_shape};
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}
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}
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std::vector<paddle::DataType> PagedAttnInferDtype(
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const paddle::DataType& q_dtype) {
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return {q_dtype};
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}
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PD_BUILD_STATIC_OP(paged_attn)
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.Inputs({"q",
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"k_cache",
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"v_cache",
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"block_table",
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"seq_lens",
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paddle::Optional("alibi_slopes"),
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paddle::Optional("k"),
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paddle::Optional("v"),
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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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"scale:float",
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"block_size:int",
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"max_context_len:int",
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"causal:bool",
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"window_left:int",
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"window_right:int",
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"softcap:float",
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"enable_cuda_graph:bool",
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"use_sqrt_alibi:bool",
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"merged_qkv:bool",
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"rope_batch_stride:int",
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"is_interleaved_rope_mode:bool"})
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.SetKernelFn(PD_KERNEL(PagedAttn))
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.SetInferShapeFn(PD_INFER_SHAPE(PagedAttnInferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(PagedAttnInferDtype));
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