f28223c9da
Replaces all Python PyTorch fallback attention with native ixformer kernels: Decode path: - ≤32K: paged_attention_v1 (5D KV layout, x=8) — verified on real BI-V100 - >32K: paged_attention_v2 (5D→4D permute) — verified 65K+ on real BI-V100 - Removes _forward_decode_pytorch Python fallback entirely Prefill path (profiling): - _run_sdpa_fallback now uses ixformer.flash_attn_varlen_func - head_dim=256 verified correct (diff<0.004) and 1.7x faster than PyTorch - Falls back to Q-tiling pure-math if ixformer unavailable Also includes: MoE kernel integration, GDN C++ kernels, diagnostic scripts, xllm upstream layer/kernel references, .dockerignore cleanup. All changes verified on real BI-V100 hardware (single card).
71 lines
2.2 KiB
C++
71 lines
2.2 KiB
C++
/* Copyright 2025 The xLLM Authors. All Rights Reserved.
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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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https://github.com/jd-opensource/xllm/blob/main/LICENSE
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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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==============================================================================*/
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#pragma once
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#include <torch/torch.h>
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#include <tuple>
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#include "framework/kv_cache/kv_cache.h"
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#include "framework/model/model_input_params.h"
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#include "layers/common/attention_metadata.h"
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namespace xllm {
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namespace layer {
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class AttentionImpl : public torch::nn::Module {
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public:
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AttentionImpl() = default;
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AttentionImpl(int64_t num_heads,
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int64_t head_size,
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float scale,
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int64_t num_kv_heads,
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int64_t sliding_window);
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std::tuple<torch::Tensor, std::optional<torch::Tensor>> forward(
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const AttentionMetadata& attn_metadata,
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torch::Tensor& query,
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torch::Tensor& key,
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torch::Tensor& value,
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KVCache& kv_cache);
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void prefill_forward(torch::Tensor& query,
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torch::Tensor& key,
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torch::Tensor& value,
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torch::Tensor& output,
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const torch::Tensor& k_cache,
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const std::optional<torch::Tensor>& v_cache,
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const AttentionMetadata& attn_metadata);
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void decoder_forward(torch::Tensor& query,
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torch::Tensor& output,
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const torch::Tensor& k_cache,
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const std::optional<torch::Tensor>& v_cache,
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const AttentionMetadata& attn_metadata);
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private:
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int64_t num_heads_;
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int64_t head_size_;
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float scale_;
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int64_t num_kv_heads_;
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int64_t sliding_window_;
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};
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TORCH_MODULE(Attention);
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} // namespace layer
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} // namespace xllm
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