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* Integrating evoformer attention * add cutlass version check * Updaate error message * add benchmark * Update * Update evoformer_attn.py * Update run_evoformer_test.py * Update evoformer_attn.py * Update run_evoformer_test.py * support more GPU archs * add copyright * add tests * Fix bugs * Update benchmark * update * Fix nvcc macro * clean code * fix formatting * fix yaml import * skip unit test when not compatible * fix yaml requirement * revert changes * update tutorial * update * fix formatting * fix format * skip evoformer attn in pre-compile-ops * revert changes * update tutorial * fix cutlass check * update tutorial * refactor tutorial * revise * Updated the Megatron-DS section (#565) * Updated the Megatron-DS section * minor fix * minor fix * minor fix * separate evoformer tutorial * Revised the ds4science landing page (#566) * Updated the Megatron-DS section * minor fix * minor fix * minor fix * Revised the landing page * Revised the landing page * Removing unused file * fix links image position * modify main page * fix doc --------- Co-authored-by: Shiyang Chen <[email protected]> Co-authored-by: Minjia Zhang <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| // Copyright (c) Microsoft Corporation. | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
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| // DeepSpeed Team | ||
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| #include <torch/extension.h> | ||
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| void attention_impl(torch::Tensor& q, | ||
| torch::Tensor& k, | ||
| torch::Tensor& v, | ||
| torch::Tensor& bias1, | ||
| torch::Tensor& bias2, | ||
| torch::Tensor& o, | ||
| torch::Tensor& lse); | ||
| void attention(torch::Tensor& q, | ||
| torch::Tensor& k, | ||
| torch::Tensor& v, | ||
| torch::Tensor& bias1, | ||
| torch::Tensor& bias2, | ||
| torch::Tensor& o, | ||
| torch::Tensor& lse) | ||
| { | ||
| attention_impl(q, k, v, bias1, bias2, o, lse); | ||
| } | ||
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| void attention_back_impl(torch::Tensor& go, | ||
| torch::Tensor& q, | ||
| torch::Tensor& k, | ||
| torch::Tensor& v, | ||
| torch::Tensor& o, | ||
| torch::Tensor& lse, | ||
| torch::Tensor& delta, | ||
| torch::Tensor& bias1, | ||
| torch::Tensor& bias2, | ||
| torch::Tensor& gq, | ||
| torch::Tensor& gk, | ||
| torch::Tensor& gv, | ||
| torch::Tensor& gb1, | ||
| torch::Tensor& gb2); | ||
| void attention_bwd(torch::Tensor& go, | ||
| torch::Tensor& q, | ||
| torch::Tensor& k, | ||
| torch::Tensor& v, | ||
| torch::Tensor& o, | ||
| torch::Tensor& lse, | ||
| torch::Tensor& delta, | ||
| torch::Tensor& bias1, | ||
| torch::Tensor& bias2, | ||
| torch::Tensor& gq, | ||
| torch::Tensor& gk, | ||
| torch::Tensor& gv, | ||
| torch::Tensor& gb1, | ||
| torch::Tensor& gb2) | ||
| { | ||
| attention_back_impl(go, q, k, v, o, lse, delta, bias1, bias2, gq, gk, gv, gb1, gb2); | ||
| } | ||
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| PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) | ||
| { | ||
| m.def("attention", &attention, ""); | ||
| m.def("attention_bwd", &attention_bwd, ""); | ||
| } |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,160 @@ | ||
| // Copyright (c) Microsoft Corporation. | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
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| // DeepSpeed Team | ||
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| #include <ATen/cuda/CUDAContext.h> | ||
| #include <torch/extension.h> | ||
| #include "gemm_kernel_utils.h" | ||
| #include "kernel_forward.h" | ||
| #include "transform/bias_broadcast.h" | ||
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| template <typename arch, | ||
| typename scalar_t, | ||
| typename torch_scalar_t, | ||
| template <typename, typename, typename> | ||
| class Broadcast1_, | ||
| template <typename, typename, typename> | ||
| class Broadcast2_> | ||
| typename std::enable_if<!CheckArch<arch, scalar_t>::value>::type attention_impl_template( | ||
| torch::Tensor& q, | ||
| torch::Tensor& k, | ||
| torch::Tensor& v, | ||
| torch::Tensor& bias1, | ||
| torch::Tensor& bias2, | ||
| torch::Tensor& o, | ||
| float* lse_ptr) | ||
| { | ||
| EVOFORMER_CHECK(false, "Unsupported GPU and data type combination") | ||
| } | ||
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| template <typename arch, | ||
| typename scalar_t, | ||
| typename torch_scalar_t, | ||
| template <typename, typename, typename> | ||
| class Broadcast1_, | ||
| template <typename, typename, typename> | ||
| class Broadcast2_> | ||
| typename std::enable_if<CheckArch<arch, scalar_t>::value>::type attention_impl_template( | ||
| torch::Tensor& q, | ||
| torch::Tensor& k, | ||
| torch::Tensor& v, | ||
| torch::Tensor& bias1, | ||
| torch::Tensor& bias2, | ||
| torch::Tensor& o, | ||
| float* lse_ptr) | ||
| { | ||
| // Attention definition goes here, replaced with BroadcastType1 and | ||
| // BroadcastType2 | ||
| using Attention = AttentionKernel<scalar_t, /* scalar_t */ | ||
| arch, /* ArchTag */ | ||
| true, /* Memory is aligned */ | ||
| 64, | ||
| 64, | ||
| true, | ||
| true, /* Supports bias */ | ||
| Broadcast1_, | ||
| Broadcast2_>; | ||
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| static_assert(!Attention::kNeedsOutputAccumulatorBuffer, | ||
| "This test does not support output accumulator buffer"); | ||
| int head_size = q.size(-1); | ||
| int head_number = q.size(-2); | ||
| int seq_length = q.size(-3); | ||
| auto q_view = q.view({-1, seq_length, head_number, head_size}); | ||
| auto k_view = k.view({-1, seq_length, head_number, head_size}); | ||
| auto v_view = v.view({-1, seq_length, head_number, head_size}); | ||
| auto o_view = o.view({-1, seq_length, head_number, head_size}); | ||
| int batch_size = q_view.size(0); | ||
| auto q_ptr = reinterpret_cast<scalar_t*>(q.data_ptr<torch_scalar_t>()); | ||
| auto k_ptr = reinterpret_cast<scalar_t*>(k.data_ptr<torch_scalar_t>()); | ||
| auto v_ptr = reinterpret_cast<scalar_t*>(v.data_ptr<torch_scalar_t>()); | ||
| auto o_ptr = reinterpret_cast<scalar_t*>(o.data_ptr<torch_scalar_t>()); | ||
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| auto bias1_ptr = reinterpret_cast<scalar_t*>(bias1.data_ptr<torch_scalar_t>()); | ||
| auto bias2_ptr = reinterpret_cast<scalar_t*>(bias2.data_ptr<torch_scalar_t>()); | ||
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| typename Attention::Params p; | ||
| { // set parameters | ||
| p.query_ptr = q_ptr; | ||
| p.key_ptr = k_ptr; | ||
| p.value_ptr = v_ptr; | ||
| p.logsumexp_ptr = lse_ptr; // Only needed for bw | ||
| p.output_accum_ptr = nullptr; | ||
| p.output_ptr = o_ptr; | ||
| p.scale = 1.0f / sqrt(float(head_size)); | ||
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| p.bias1_ptr = bias1_ptr; | ||
| p.bias2_ptr = bias2_ptr; | ||
| p.B = q.size(0); | ||
| p.N = q.size(1); | ||
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| p.num_heads = head_number; | ||
| p.num_batches = batch_size; | ||
| p.head_dim = head_size; | ||
| p.head_dim_value = head_size; | ||
| p.num_queries = seq_length; | ||
| p.num_keys = seq_length; | ||
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| // All tensors are in BMHK shapes | ||
| p.q_strideH = q_view.stride(-2); | ||
| p.k_strideH = k_view.stride(-2); | ||
| p.v_strideH = v_view.stride(-2); | ||
| p.q_strideM = q_view.stride(-3); | ||
| p.k_strideM = k_view.stride(-3); | ||
| p.v_strideM = v_view.stride(-3); | ||
| p.o_strideM = o_view.stride(-3); | ||
| p.q_strideB = q_view.stride(-4); | ||
| p.k_strideB = k_view.stride(-4); | ||
| p.v_strideB = v_view.stride(-4); | ||
| } | ||
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| constexpr auto kernel_fn = attention_kernel_batched_impl<Attention>; | ||
| int smem_bytes = sizeof(typename Attention::SharedStorage); | ||
| if (smem_bytes > 0xc000) { | ||
| cudaFuncSetAttribute(kernel_fn, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_bytes); | ||
| } | ||
| if (!Attention::check_supported(p)) { throw std::runtime_error("Parameters not supported"); } | ||
| kernel_fn<<<p.getBlocksGrid(), p.getThreadsGrid(), smem_bytes>>>(p); | ||
| } | ||
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| #define CODE(scalar_t, torch_scalar_t) \ | ||
| do { \ | ||
| if (bias1.size(0) == 0 && bias2.size(0) == 0) { \ | ||
| attention_impl_template<ArchTag, \ | ||
| scalar_t, \ | ||
| torch_scalar_t, \ | ||
| BroadcastNoLoad, \ | ||
| BroadcastNoLoad>(q, k, v, bias1, bias2, o, lse_ptr); \ | ||
| } else if (bias1.size(0) == 0) { \ | ||
| attention_impl_template<ArchTag, \ | ||
| scalar_t, \ | ||
| torch_scalar_t, \ | ||
| BroadcastNoLoad, \ | ||
| BroadcastB>(q, k, v, bias1, bias2, o, lse_ptr); \ | ||
| } else if (bias2.size(0) == 0) { \ | ||
| attention_impl_template<ArchTag, \ | ||
| scalar_t, \ | ||
| torch_scalar_t, \ | ||
| BroadcastA, \ | ||
| BroadcastNoLoad>(q, k, v, bias1, bias2, o, lse_ptr); \ | ||
| } else { \ | ||
| attention_impl_template<ArchTag, scalar_t, torch_scalar_t, BroadcastA, BroadcastB>( \ | ||
| q, k, v, bias1, bias2, o, lse_ptr); \ | ||
| } \ | ||
| } while (0) | ||
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| // Function to select and call the correct template based on biases sizes | ||
| void attention_impl(torch::Tensor& q, | ||
| torch::Tensor& k, | ||
| torch::Tensor& v, | ||
| torch::Tensor& bias1, | ||
| torch::Tensor& bias2, | ||
| torch::Tensor& o, | ||
| torch::Tensor& lse) | ||
| { | ||
| auto lse_ptr = lse.size(0) == 0 ? nullptr : reinterpret_cast<float*>(lse.data_ptr<float>()); | ||
| cudaDeviceProp* prop = at::cuda::getCurrentDeviceProperties(); | ||
| DISPATCH_ARCHTAG(prop->major * 10 + prop->minor, | ||
| DISPATCH_TYPES(q, ([&]() { CODE(scalar_t, torch_scalar_t); }))); | ||
| } |
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