From 734d8991c845664c5138a9f69704fd058a51b3c1 Mon Sep 17 00:00:00 2001 From: Jeff Rasley Date: Fri, 29 May 2020 13:15:36 -0700 Subject: [PATCH] Transformer kernel release (#242) * Transformer kernels release Co-authored-by: Shaden Smith Co-authored-by: Elton Zheng Co-authored-by: Reza Yazdani Co-authored-by: RezaYazdaniAminabadi <44502768+RezaYazdaniAminabadi@users.noreply.github.com> Co-authored-by: Tunji Ruwase Co-authored-by: Shaden Smith Co-authored-by: Shaden Smith Co-authored-by: Samyam Rajbhandari Co-authored-by: Shaden Smith Co-authored-by: Jeff Rasley Co-authored-by: Samyam Rajbhandari Co-authored-by: Shaden Smith Co-authored-by: Elton Zheng Co-authored-by: Reza Yazdani Co-authored-by: RezaYazdaniAminabadi <44502768+RezaYazdaniAminabadi@users.noreply.github.com> Co-authored-by: Tunji Ruwase Co-authored-by: Shaden Smith Co-authored-by: Samyam Rajbhandari --- .clang-format | 155 ++ .pre-commit-config.yaml | 6 + DeepSpeedExamples | 2 +- basic_install_test.py | 10 +- csrc/fused_lamb_cuda.cpp | 43 - csrc/fused_lamb_cuda_kernel.cu | 511 ---- csrc/includes/StopWatch.h | 98 + csrc/includes/Timer.h | 47 + csrc/includes/context.h | 177 ++ csrc/includes/cublas_wrappers.h | 69 + csrc/includes/custom_cuda_layers.h | 266 ++ csrc/includes/dropout.h | 76 + csrc/includes/ds_transformer_cuda.h | 174 ++ csrc/includes/feed_forward.h | 93 + csrc/includes/gelu.h | 43 + csrc/includes/gemm_test.h | 293 +++ csrc/includes/general_kernels.h | 47 + csrc/includes/normalize_layer.h | 227 ++ csrc/includes/softmax.h | 58 + csrc/includes/strided_batch_gemm.h | 170 ++ csrc/includes/type_shim.h | 109 + csrc/lamb/fused_lamb_cuda.cpp | 109 + csrc/lamb/fused_lamb_cuda_kernel.cu | 469 ++++ csrc/transformer/cublas_wrappers.cu | 178 ++ csrc/transformer/dropout_kernels.cu | 720 ++++++ csrc/transformer/ds_transformer_cuda.cpp | 974 ++++++++ csrc/transformer/gelu_kernels.cu | 336 +++ csrc/transformer/general_kernels.cu | 416 ++++ csrc/transformer/normalize_kernels.cu | 2159 +++++++++++++++++ csrc/transformer/softmax_kernels.cu | 596 +++++ csrc/transformer/transform_kernels.cu | 418 ++++ csrc/type_shim.h | 131 - deepspeed/__init__.py | 2 + deepspeed/pt/deepspeed_cuda.py | 520 ++++ deepspeed/pt/deepspeed_fused_lamb.py | 2 +- deepspeed/pt/deepspeed_run.py | 4 +- docs/_posts/2020-05-19-bert-record.md | 7 +- .../2020-05-28-fastest-bert-training.md | 268 ++ docs/_tutorials/bert-finetuning.md | 189 ++ docs/_tutorials/bert-pretraining.md | 140 +- docs/_tutorials/transformer_kernel.md | 124 + .../images/end-to-end-bert-training.PNG | Bin 0 -> 86168 bytes docs/assets/images/layernorm_animation.gif | Bin 0 -> 1419354 bytes docs/assets/images/layernorm_deepspeed.gif | Bin 0 -> 2554469 bytes docs/assets/images/layernorm_ds.png | Bin 0 -> 247279 bytes docs/assets/images/layernorm_pytorch.gif | Bin 0 -> 1832912 bytes docs/assets/images/layernorm_torch.png | Bin 0 -> 252399 bytes docs/assets/images/qkv_fusion.png | Bin 0 -> 35926 bytes docs/assets/images/single-gpu-throughput.png | Bin 0 -> 36785 bytes docs/assets/images/softmax_animation.gif | Bin 0 -> 3180437 bytes docs/assets/images/softmax_deepspeed.gif | Bin 0 -> 2412470 bytes docs/assets/images/softmax_ds.png | Bin 0 -> 156048 bytes docs/assets/images/softmax_pytorch.gif | Bin 0 -> 1352070 bytes docs/assets/images/softmax_torch.png | Bin 0 -> 152045 bytes .../assets/images/transformer_kernel_perf.png | Bin 0 -> 101441 bytes .../images/transformer_kernel_perf_seq128.PNG | Bin 0 -> 75233 bytes .../images/transformer_kernel_perf_seq512.PNG | Bin 0 -> 62463 bytes docs/assets/images/transformer_preln_arch.png | Bin 0 -> 43008 bytes docs/code-docs/source/checkpointing.rst | 26 - docs/code-docs/source/conf.py | 9 +- docs/code-docs/source/index.rst | 9 +- docs/code-docs/source/kernel.rst | 17 + requirements.txt | 1 + setup.py | 86 +- tests/unit/modeling.py | 1578 ++++++++++++ tests/unit/modelingpreln.py | 1665 +++++++++++++ tests/unit/test_cuda_backward.py | 322 +++ tests/unit/test_cuda_forward.py | 325 +++ 68 files changed, 13698 insertions(+), 776 deletions(-) create mode 100755 .clang-format delete mode 100644 csrc/fused_lamb_cuda.cpp delete mode 100644 csrc/fused_lamb_cuda_kernel.cu create mode 100644 csrc/includes/StopWatch.h create mode 100644 csrc/includes/Timer.h create mode 100644 csrc/includes/context.h create mode 100644 csrc/includes/cublas_wrappers.h create mode 100755 csrc/includes/custom_cuda_layers.h create mode 100644 csrc/includes/dropout.h create mode 100755 csrc/includes/ds_transformer_cuda.h create mode 100644 csrc/includes/feed_forward.h create mode 100644 csrc/includes/gelu.h create mode 100644 csrc/includes/gemm_test.h create mode 100644 csrc/includes/general_kernels.h create mode 100644 csrc/includes/normalize_layer.h create mode 100644 csrc/includes/softmax.h create mode 100644 csrc/includes/strided_batch_gemm.h create mode 100644 csrc/includes/type_shim.h create mode 100644 csrc/lamb/fused_lamb_cuda.cpp create mode 100644 csrc/lamb/fused_lamb_cuda_kernel.cu create mode 100644 csrc/transformer/cublas_wrappers.cu create mode 100644 csrc/transformer/dropout_kernels.cu create mode 100755 csrc/transformer/ds_transformer_cuda.cpp create mode 100755 csrc/transformer/gelu_kernels.cu create mode 100644 csrc/transformer/general_kernels.cu create mode 100755 csrc/transformer/normalize_kernels.cu create mode 100644 csrc/transformer/softmax_kernels.cu create mode 100644 csrc/transformer/transform_kernels.cu delete mode 100644 csrc/type_shim.h create mode 100755 deepspeed/pt/deepspeed_cuda.py create mode 100644 docs/_posts/2020-05-28-fastest-bert-training.md create mode 100755 docs/_tutorials/bert-finetuning.md mode change 100644 => 100755 docs/_tutorials/bert-pretraining.md create mode 100755 docs/_tutorials/transformer_kernel.md create mode 100644 docs/assets/images/end-to-end-bert-training.PNG create mode 100644 docs/assets/images/layernorm_animation.gif create mode 100644 docs/assets/images/layernorm_deepspeed.gif create mode 100644 docs/assets/images/layernorm_ds.png create mode 100644 docs/assets/images/layernorm_pytorch.gif create mode 100644 docs/assets/images/layernorm_torch.png create mode 100644 docs/assets/images/qkv_fusion.png create mode 100644 docs/assets/images/single-gpu-throughput.png create mode 100644 docs/assets/images/softmax_animation.gif create mode 100644 docs/assets/images/softmax_deepspeed.gif create mode 100644 docs/assets/images/softmax_ds.png create mode 100644 docs/assets/images/softmax_pytorch.gif create mode 100644 docs/assets/images/softmax_torch.png create mode 100644 docs/assets/images/transformer_kernel_perf.png create mode 100644 docs/assets/images/transformer_kernel_perf_seq128.PNG create mode 100644 docs/assets/images/transformer_kernel_perf_seq512.PNG create mode 100644 docs/assets/images/transformer_preln_arch.png delete mode 100644 docs/code-docs/source/checkpointing.rst create mode 100644 docs/code-docs/source/kernel.rst mode change 100644 => 100755 setup.py create mode 100755 tests/unit/modeling.py create mode 100755 tests/unit/modelingpreln.py create mode 100755 tests/unit/test_cuda_backward.py create mode 100755 tests/unit/test_cuda_forward.py diff --git a/.clang-format b/.clang-format new file mode 100755 index 000000000000..9b547c1a18fe --- /dev/null +++ b/.clang-format @@ -0,0 +1,155 @@ +--- +# Refer to the following link for the explanation of each params: +# http://releases.llvm.org/8.0.0/tools/clang/docs/ClangFormatStyleOptions.html +Language: Cpp +# BasedOnStyle: Google +AccessModifierOffset: -4 +AlignAfterOpenBracket: Align +AlignConsecutiveAssignments: false +AlignConsecutiveDeclarations: false +AlignEscapedNewlines: Left +AlignOperands: true +AlignTrailingComments: true +AllowAllParametersOfDeclarationOnNextLine: false +AllowShortBlocksOnASingleLine: true +AllowShortCaseLabelsOnASingleLine: true +AllowShortFunctionsOnASingleLine: All +AllowShortIfStatementsOnASingleLine: true +AllowShortLoopsOnASingleLine: true +# This is deprecated +AlwaysBreakAfterDefinitionReturnType: None +AlwaysBreakAfterReturnType: None +AlwaysBreakBeforeMultilineStrings: true +AlwaysBreakTemplateDeclarations: Yes +BinPackArguments: false +BinPackParameters: false +BraceWrapping: + AfterClass: false + AfterControlStatement: false + AfterEnum: false + AfterFunction: false + AfterNamespace: false + AfterObjCDeclaration: false + AfterStruct: false + AfterUnion: false + AfterExternBlock: false + BeforeCatch: false + BeforeElse: false + IndentBraces: false + # disabling the below splits, else, they'll just add to the vertical length of source files! + SplitEmptyFunction: false + SplitEmptyRecord: false + SplitEmptyNamespace: false +BreakBeforeBinaryOperators: None +BreakBeforeBraces: WebKit +BreakBeforeInheritanceComma: false +BreakInheritanceList: BeforeColon +BreakBeforeTernaryOperators: true +BreakConstructorInitializersBeforeComma: false +BreakConstructorInitializers: BeforeColon +BreakAfterJavaFieldAnnotations: false +BreakStringLiterals: true +ColumnLimit: 100 +CommentPragmas: '^ IWYU pragma:' +CompactNamespaces: false +ConstructorInitializerAllOnOneLineOrOnePerLine: true +# Kept the below 2 to be the same as `IndentWidth` to keep everything uniform +ConstructorInitializerIndentWidth: 4 +ContinuationIndentWidth: 4 +Cpp11BracedListStyle: true +DerivePointerAlignment: false +DisableFormat: false +ExperimentalAutoDetectBinPacking: false +FixNamespaceComments: true +ForEachMacros: + - foreach + - Q_FOREACH + - BOOST_FOREACH +IncludeBlocks: Preserve +IncludeCategories: + - Regex: '^' + Priority: 2 + - Regex: '^<.*\.h>' + Priority: 1 + - Regex: '^<.*' + Priority: 2 + - Regex: '.*' + Priority: 3 +IncludeIsMainRegex: '([-_](test|unittest))?$' +IndentCaseLabels: true +IndentPPDirectives: None +IndentWidth: 4 +IndentWrappedFunctionNames: false +JavaScriptQuotes: Leave +JavaScriptWrapImports: true +KeepEmptyLinesAtTheStartOfBlocks: false +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +ObjCBinPackProtocolList: Never +ObjCBlockIndentWidth: 4 +ObjCSpaceAfterProperty: false +ObjCSpaceBeforeProtocolList: true +PenaltyBreakAssignment: 4 +PenaltyBreakBeforeFirstCallParameter: 1 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +PenaltyBreakTemplateDeclaration: 10 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 200 +PointerAlignment: Left +RawStringFormats: + - Language: Cpp + Delimiters: + - cc + - CC + - cpp + - Cpp + - CPP + - 'c++' + - 'C++' + CanonicalDelimiter: '' + - Language: TextProto + Delimiters: + - pb + - PB + - proto + - PROTO + EnclosingFunctions: + - EqualsProto + - EquivToProto + - PARSE_PARTIAL_TEXT_PROTO + - PARSE_TEST_PROTO + - PARSE_TEXT_PROTO + - ParseTextOrDie + - ParseTextProtoOrDie + CanonicalDelimiter: '' + BasedOnStyle: google +# Enabling comment reflow causes doxygen comments to be messed up in their formats! +ReflowComments: true +SortIncludes: true +SortUsingDeclarations: true +SpaceAfterCStyleCast: false +SpaceAfterTemplateKeyword: true +SpaceBeforeAssignmentOperators: true +SpaceBeforeCpp11BracedList: false +SpaceBeforeCtorInitializerColon: true +SpaceBeforeInheritanceColon: true +SpaceBeforeParens: ControlStatements +SpaceBeforeRangeBasedForLoopColon: true +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 2 +SpacesInAngles: false +SpacesInContainerLiterals: true +SpacesInCStyleCastParentheses: false +SpacesInParentheses: false +SpacesInSquareBrackets: false +Standard: Cpp11 +StatementMacros: + - Q_UNUSED + - QT_REQUIRE_VERSION +# Be consistent with indent-width, even for people who use tab for indentation! +TabWidth: 4 +UseTab: Never diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e8fd67bb72c8..571105d41230 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -17,3 +17,9 @@ repos: hooks: - id: yapf exclude: "examples/" + +- repo: https://gitlab.com/daverona/pre-commit-cpp + rev: 0.6.0 + hooks: + - id: clang-format # formatter of C/C++ code based on a style guide: LLVM, Google, Chromium, Mozilla, and WebKit available + args: [] diff --git a/DeepSpeedExamples b/DeepSpeedExamples index 274787a189b2..10b60d107f17 160000 --- a/DeepSpeedExamples +++ b/DeepSpeedExamples @@ -1 +1 @@ -Subproject commit 274787a189b265814ed75dd5ddeae2dce026ea88 +Subproject commit 10b60d107f172d1331924e1dcb70f4da3ebf5dd1 diff --git a/basic_install_test.py b/basic_install_test.py index 8079b4fb8d3f..966b124f5813 100644 --- a/basic_install_test.py +++ b/basic_install_test.py @@ -18,8 +18,8 @@ raise err try: - fused_lamb = importlib.import_module('fused_lamb_cuda') - print('deepspeed kernels successfully installed') + fused_lamb = importlib.import_module('deepspeed_lamb_cuda') + print('deepspeed fused lamb kernels successfully installed') except Exception as err: raise err @@ -28,3 +28,9 @@ print("using old-style apex") except ImportError: print("using new-style apex") + +try: + ds_transformer = importlib.import_module('deepspeed_transformer_cuda') + print('deepspeed transformer kernels successfully installed') +except Exception as err: + raise err diff --git a/csrc/fused_lamb_cuda.cpp b/csrc/fused_lamb_cuda.cpp deleted file mode 100644 index 605ae6aabdbb..000000000000 --- a/csrc/fused_lamb_cuda.cpp +++ /dev/null @@ -1,43 +0,0 @@ -/* Copyright 2019 The Microsoft DeepSpeed Team */ -#include - -// CUDA forward declaration -void fused_lamb_cuda(at::Tensor & p, at::Tensor & p_copy, at::Tensor & m, at::Tensor & v, at::Tensor & g, - float lr, float beta1, float beta2, float max_coeff, float min_coeff, float eps, float grad_scale, int step, int mode, int bias_correction, float decay, - at::Tensor & w_l2_i, at::Tensor & u_l2_i, at::Tensor & lamb_coeff_val ); - -#define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") -#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) - -// C++ interface -at::Tensor lamb(at::Tensor & p, at::Tensor & p_copy, at::Tensor & m, at::Tensor & v, at::Tensor & g, float lr, float beta1, float beta2, float max_coeff, float min_coeff, float eps, float grad_scale, int step, int mode, int bias_correction, float decay) { - CHECK_INPUT(p); - if (p_copy.numel() > 0) CHECK_INPUT(p_copy); - CHECK_INPUT(m); - CHECK_INPUT(v); - CHECK_INPUT(g); - int64_t num_elem = p.numel(); - AT_ASSERTM(m.numel() == num_elem, "number of elements in m and p tensors should be equal"); - AT_ASSERTM(v.numel() == num_elem, "number of elements in v and p tensors should be equal"); - AT_ASSERTM(g.numel() == num_elem, "number of elements in g and p tensors should be equal"); - AT_ASSERTM(p_copy.numel() == num_elem || p_copy.numel() == 0, "number of elements in p_copy and p tensors should be equal, or p_copy should be empty"); - - //intermediate for weight L2 reduction - //make sure that the threads per block is at least 512 during the kernel launch otherwise the behavious is unexpected - at::Tensor w_l2_i = at::empty({512}, p.options().dtype(p.type().scalarType()==at::ScalarType::Half ? at::ScalarType::Float : p.type().scalarType())); - - //intermediate for update L2 reduction - //make sure that the threads per block is at least 512 during the kernel launch otherwise the behavious is unexpected - at::Tensor u_l2_i = at::empty({512}, p.options().dtype(p.type().scalarType()==at::ScalarType::Half ? at::ScalarType::Float : p.type().scalarType())); - - at::Tensor lamb_coeff_val = at::empty({1}, p.options().dtype(p.type().scalarType()==at::ScalarType::Half ? at::ScalarType::Float : p.type().scalarType())); - - fused_lamb_cuda(p, p_copy, m, v, g, lr, beta1, beta2, max_coeff, min_coeff, eps, grad_scale, step, mode, bias_correction, decay, w_l2_i, u_l2_i, lamb_coeff_val); - - return lamb_coeff_val; -} - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("lamb", &lamb, "Adam optimized CUDA implementation with LAMB."); -} diff --git a/csrc/fused_lamb_cuda_kernel.cu b/csrc/fused_lamb_cuda_kernel.cu deleted file mode 100644 index b79f5af82332..000000000000 --- a/csrc/fused_lamb_cuda_kernel.cu +++ /dev/null @@ -1,511 +0,0 @@ -/* Copyright 2019 The Microsoft DeepSpeed Team */ -#include "ATen/ATen.h" -#include "ATen/cuda/CUDAContext.h" -#include "ATen/cuda/detail/IndexUtils.cuh" -#include -#include -#include -#include -#include "ATen/TensorUtils.h" -//#include "ATen/Type.h" -#include "ATen/AccumulateType.h" -#include - -#include - -//#include -#include -#include -#include - -namespace cg = cooperative_groups; - -// Utility class used to avoid linker errors with extern -// unsized shared memory arrays with templated type -namespace { - // This is the un-specialized struct. Note that we prevent instantiation of this - // struct by putting an undefined symbol in the function body so it won't compile. - template - struct SharedMemory - { - // Ensure that we won't compile any un-specialized types - __device__ inline operator T *() - { - extern __device__ void error(void); - error(); - return NULL; - } - }; - - template <> - struct SharedMemory - { - __device__ inline operator float *() - { - extern __shared__ float s_float[]; - return s_float; - } - }; - - template <> - struct SharedMemory - { - __device__ inline operator double *() - { - extern __shared__ double s_double[]; - return s_double; - } - }; - } - -#include "type_shim.h" - -typedef enum{ - ADAM_MODE_0 =0, // eps under square root - ADAM_MODE_1 =1 // eps outside square root -} adamMode_t; - - -//s_a and s_b are in shared memory -//g_a and g_b are in shared memory -template -__device__ void -reduce_block_in_shared_memory(T *s_a, T *s_b, T* g_a, T* g_b) -{ - // Handle to thread block group - cg::thread_block cta = cg::this_thread_block(); - - // perform block reduction in shared memory, - unsigned int tid = cta.thread_rank(); - - T a_sum = s_a[tid]; - T b_sum = s_b[tid]; - - cg::sync(cta); - - // do reduction in shared mem - if ((blockSize >= 512) && (tid < 256)) - { - s_a[tid] = a_sum = a_sum + s_a[tid + 256]; - s_b[tid] = b_sum = b_sum + s_b[tid + 256]; - - } - - cg::sync(cta); - - if ((blockSize >= 256) && (tid < 128)) - { - s_a[tid] = a_sum = a_sum + s_a[tid + 128]; - s_b[tid] = b_sum = b_sum + s_b[tid + 128]; - - } - - cg::sync(cta); - - if ((blockSize >= 128) && (tid < 64)) - { - s_a[tid] = a_sum = a_sum + s_a[tid + 64]; - s_b[tid] = b_sum = b_sum + s_b[tid + 64]; - - } - - cg::sync(cta); - -#if (__CUDA_ARCH__ >= 300 ) - if ( tid < 32 ) - { - cg::coalesced_group active = cg::coalesced_threads(); - - // Fetch final intermediate sum from 2nd warp - if (blockSize >= 64) - { - a_sum = a_sum + s_a[tid + 32]; - b_sum = b_sum + s_b[tid + 32]; - } - - // Reduce final warp using shuffle - for (int offset = warpSize/2; offset > 0; offset /= 2) - { - a_sum += active.shfl_down(a_sum, offset); - b_sum += active.shfl_down(b_sum, offset); - - } - } -#else - if ((blockSize >= 64) && (tid < 32)) - { - s_a[tid] = a_sum = a_sum + s_a[tid + 32]; - s_b[tid] = b_sum = b_sum + s_b[tid + 32]; - - } - - cg::sync(cta); - - if ((blockSize >= 32) && (tid < 16)) - { - s_a[tid] = a_sum = a_sum + s_a[tid + 16]; - s_b[tid] = b_sum = b_sum + s_b[tid + 16]; - - } - - cg::sync(cta); - - if ((blockSize >= 16) && (tid < 8)) - { - s_a[tid] = a_sum = a_sum + s_a[tid + 8]; - s_b[tid] = b_sum = b_sum + s_b[tid + 8]; - - } - - cg::sync(cta); - - if ((blockSize >= 8) && (tid < 4)) - { - s_a[tid] = a_sum = a_sum + s_a[tid + 4]; - s_b[tid] = b_sum = b_sum + s_b[tid + 4]; - - } - - cg::sync(cta); - - if ((blockSize >= 4) && (tid < 2)) - { - s_a[tid] = a_sum = a_sum + s_a[tid + 2]; - s_b[tid] = b_sum = b_sum + s_b[tid + 2]; - - } - - cg::sync(cta); - - if ((blockSize >= 2) && (tid < 1)) - { - s_a[tid] = a_sum = a_sum + s_a[tid + 1]; - s_b[tid] = b_sum = b_sum + s_b[tid + 1]; - - } - - cg::sync(cta); - -#endif - - // write result for this block to global mem - if (tid == 0){ - g_a[blockIdx.x] = (T)a_sum; - g_b[blockIdx.x] = (T)b_sum; - } -} - -template -__device__ void reduce_two_vectors_in_register(T a, T b, T* g_a, T* g_b){ - - const int threadIdInBlock = cg::this_thread_block().thread_rank(); - - T *s_a = SharedMemory(); - T *s_b = SharedMemory() + cg::this_thread_block().size(); - - s_a[threadIdInBlock] = a; - s_b[threadIdInBlock] = b; - - reduce_block_in_shared_memory(s_a, s_b ,g_a, g_b); - -} - - -template -__global__ void lamb_cuda_kernel_part1( - T* __restrict__ p, - GRAD_T* __restrict__ p_copy, // For mixed precision training, pass NULL if not needed - T* __restrict__ m, - T* __restrict__ v, - const GRAD_T * __restrict__ g, - const float b1, - const float b2, - const float eps, - const float grad_scale, - const float step_size, - const size_t tsize, - adamMode_t mode, - const float decay, - T* __restrict__ w_l2_i, - T* __restrict__ u_l2_i) -{ - //Assuming 2D grids and 2D blocks - const int blockId = gridDim.x * blockIdx.y + blockIdx.x; - const int threadsPerBlock = blockDim.x * blockDim.y; - const int threadIdInBlock = cg::this_thread_block().thread_rank(); - const int i = (blockId * threadsPerBlock + threadIdInBlock); - const int totThreads = gridDim.x*gridDim.y*threadsPerBlock; - - - T reg_w = 0; - T reg_u = 0; - - for (int j = i; j < tsize; j+=totThreads) { - T scaled_grad = g[j]/grad_scale; - T pj = p[j]; - m[j] = b1*m[j] + (1-b1)*scaled_grad; - v[j] = b2*v[j] + (1-b2)*scaled_grad*scaled_grad; - float denom; - if (mode == ADAM_MODE_0) - denom = sqrtf(v[j] + eps); - else // Mode 1 - denom = sqrtf(v[j]) + eps; - T update = (m[j]/denom) + (decay*p[j]); - - reg_u += update * update; - reg_w += pj * pj; - - } - - reduce_two_vectors_in_register(reg_w, reg_u, w_l2_i, u_l2_i); -} - -template -__global__ void lamb_cuda_kernel_part2( - const size_t tsize, - T* __restrict__ g_a, - T* __restrict__ g_b) -{ - - T *s_a = SharedMemory() ; - T *s_b = SharedMemory() + cg::this_thread_block().size(); - - const int threadIdInBlock = cg::this_thread_block().thread_rank(); - - s_a[threadIdInBlock] = g_a[threadIdInBlock]; - s_b[threadIdInBlock] = g_b[threadIdInBlock]; - - if (threadIdInBlock >= tsize){ - s_a[threadIdInBlock] = 0.0; - s_b[threadIdInBlock] = 0.0; - } - - reduce_block_in_shared_memory(s_a, s_b, g_a, g_b); -} - - - template - __global__ void lamb_cuda_kernel_part3( - T* __restrict__ p, - GRAD_T* __restrict__ p_copy, // For mixed precision training, pass NULL if not needed - T* __restrict__ m, - T* __restrict__ v, - const GRAD_T * __restrict__ g, - const float b1, - const float b2, - const float max_coeff, - const float min_coeff, - const float eps, - const float grad_scale, - const float step_size, - const size_t tsize, - adamMode_t mode, - const float decay, - T* __restrict__ w_l2_i, - T* __restrict__ u_l2_i, - T* __restrict__ lamb_coeff_val) -{ - - //Assuming 2D grids and 2D blocks - const int blockId = gridDim.x * blockIdx.y + blockIdx.x; - const int threadsPerBlock = blockDim.x * blockDim.y; - const int threadIdInBlock = cg::this_thread_block().thread_rank(); - const int i = (blockId * threadsPerBlock + threadIdInBlock); - const int totThreads = gridDim.x*gridDim.y*threadsPerBlock; - - T reg_w = sqrtf(w_l2_i[0]); - T reg_u = sqrtf(u_l2_i[0]); - - float lamb_coeff = 1.0; - - if (reg_w !=0 and reg_u !=0){ - lamb_coeff = reg_w/reg_u; - if (lamb_coeff > max_coeff){ - lamb_coeff = max_coeff; - } - if (lamb_coeff < min_coeff){ - lamb_coeff = min_coeff; - } - } - - if(blockId == 0 and threadIdInBlock == 0) - { - lamb_coeff_val[0] = lamb_coeff; - //printf("Cuda Lamb Coeff is %.6f \n",lamb_coeff); - } - - for (int j = i; j < tsize; j+=totThreads) { - T pj = (float)p[j]; - T mj = m[j]; - T vj = v[j]; - float denom; - if (mode == ADAM_MODE_0) - denom = sqrtf(vj + eps); - else // Mode 1 - denom = sqrtf(vj) + eps; - T update = (mj/denom) + (decay*pj); - - pj = pj - (step_size * lamb_coeff * update); - p[j] = pj; - if (p_copy != NULL) p_copy[j] = (GRAD_T) pj; - } -} - -void fused_lamb_cuda( - at::Tensor & p, - at::Tensor & p_copy, - at::Tensor & m, - at::Tensor & v, - at::Tensor & g, - float lr, - float beta1, - float beta2, - float max_coeff, - float min_coeff, - float eps, - float grad_scale, - int step, - int mode, - int bias_correction, - float decay, - at::Tensor & w_l2_i, - at::Tensor & u_l2_i, - at::Tensor & lamb_coeff) -{ -// using namespace at; - - //Get tensor size - int tsize = p.numel(); - //Determine #threads and #blocks - const int threadsPerBlock = 512; - int num_blocks = (tsize+threadsPerBlock-1)/threadsPerBlock; - if (num_blocks > 512) num_blocks=512; - - int smemsize = 0; - - if (p.type().scalarType() == at::ScalarType::Double) - smemsize = 2 * threadsPerBlock * sizeof(double); - else - smemsize = 2 * threadsPerBlock * sizeof(float); - - const dim3 blocks(num_blocks); - const dim3 threads(threadsPerBlock); - - AT_ASSERTM(at::cuda::detail::canUse32BitIndexMath(p), "parameter tensor is too large to be indexed with int32"); - //Constants - float step_size = 0; - if (bias_correction == 1) { - const float bias_correction1 = 1 - std::pow(beta1, step); - const float bias_correction2 = 1 - std::pow(beta2, step); - step_size = lr * std::sqrt(bias_correction2)/bias_correction1; - } - else { - step_size = lr; - } - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - - if (g.type().scalarType() == at::ScalarType::Half) { -//all other values should be fp32 for half gradients - AT_ASSERTM(p.type().scalarType() == at::ScalarType::Float, "expected parameter to be of float type"); -//dispatch is done on the gradient type - using namespace at; // prevents "toString is undefined" errors - AT_DISPATCH_FLOATING_TYPES_AND_HALF(g.scalar_type(), "lamb_cuda_kernel", ([&] { - using accscalar_t = at::acc_type; - - lamb_cuda_kernel_part1<<>>( - p.data(), - p_copy.numel() ? p_copy.data() : NULL, - m.data(), - v.data(), - g.data(), - beta1, - beta2, - eps, - grad_scale, - step_size, - tsize, - (adamMode_t) mode, - decay, - w_l2_i.data(), - u_l2_i.data()); - - lamb_cuda_kernel_part2<<<1,threadsPerBlock, smemsize, stream>>>( - num_blocks, - w_l2_i.data(), - u_l2_i.data()); - - lamb_cuda_kernel_part3<<>>( - p.data(), - p_copy.numel() ? p_copy.data() : NULL, - m.data(), - v.data(), - g.data(), - beta1, - beta2, - max_coeff, - min_coeff, - eps, - grad_scale, - step_size, - tsize, - (adamMode_t) mode, - decay, - w_l2_i.data(), - u_l2_i.data(), - lamb_coeff.data()); - - })); - } else { - using namespace at; - AT_DISPATCH_FLOATING_TYPES(g.scalar_type(), "lamb_cuda_kernel", ([&] { - - lamb_cuda_kernel_part1<<>>( - p.data(), - NULL, //don't output p_copy for fp32, it's wasted write - m.data(), - v.data(), - g.data(), - beta1, - beta2, - eps, - grad_scale, - step_size, - tsize, - (adamMode_t) mode, - decay, - w_l2_i.data(), - u_l2_i.data()); - - - lamb_cuda_kernel_part2<<<1,threadsPerBlock, smemsize, stream>>>( - num_blocks, - w_l2_i.data(), - u_l2_i.data()); - - lamb_cuda_kernel_part3<<>>( - p.data(), - NULL, //don't output p_copy for fp32, it's wasted write - m.data(), - v.data(), - g.data(), - beta1, - beta2, - max_coeff, - min_coeff, - eps, - grad_scale, - step_size, - tsize, - (adamMode_t) mode, - decay, - w_l2_i.data(), - u_l2_i.data(), - lamb_coeff.data()); - - })); - } - THCudaCheck(cudaGetLastError()); - -} - -//template __device__ void reduce_two_vectors_in_register(float a, float b, float* g_a, float* g_b, cg::grid_group &cgg); diff --git a/csrc/includes/StopWatch.h b/csrc/includes/StopWatch.h new file mode 100644 index 000000000000..9bf0401ebc78 --- /dev/null +++ b/csrc/includes/StopWatch.h @@ -0,0 +1,98 @@ +#pragma once +#ifdef _WIN32 +#include +#else +#include +#endif + +#ifdef _WIN32 + +class Stopwatch { +private: + double m_total_time; + LARGE_INTEGER m_start_time; + +public: + Stopwatch() { m_total_time = 0.0; } + + ~Stopwatch() {} + + void Reset() { m_total_time = 0.0; } + + void Start() { QueryPerformanceCounter(&m_start_time); } + + void Restart() + { + m_total_time = 0.0; + QueryPerformanceCounter(&m_start_time); + } + + void Stop() + { + LARGE_INTEGER frequency; + LARGE_INTEGER stop_time; + QueryPerformanceFrequency(&frequency); + QueryPerformanceCounter(&stop_time); + m_total_time += + ((double)(stop_time.QuadPart - m_start_time.QuadPart) / (double)frequency.QuadPart); + } + + double GetTimeInSeconds() { return m_total_time; } +}; + +#else + +class Stopwatch { +private: + double m_total_time; + struct timespec m_start_time; + bool m_is_started; + +public: + Stopwatch() + { + m_total_time = 0.0; + m_is_started = false; + } + + ~Stopwatch() {} + + void Reset() { m_total_time = 0.0; } + + void Start() + { + clock_gettime(CLOCK_MONOTONIC, &m_start_time); + m_is_started = true; + } + + void Restart() + { + m_total_time = 0.0; + clock_gettime(CLOCK_MONOTONIC, &m_start_time); + m_is_started = true; + } + + void Stop() + { + if (m_is_started) { + m_is_started = false; + + struct timespec end_time; + clock_gettime(CLOCK_MONOTONIC, &end_time); + + m_total_time += (double)(end_time.tv_sec - m_start_time.tv_sec) + + (double)(end_time.tv_nsec - m_start_time.tv_nsec) / 1e9; + } + } + + double GetTimeInSeconds() + { + if (m_is_started) { + Stop(); + Start(); + } + return m_total_time; + } +}; + +#endif diff --git a/csrc/includes/Timer.h b/csrc/includes/Timer.h new file mode 100644 index 000000000000..7c20854a056a --- /dev/null +++ b/csrc/includes/Timer.h @@ -0,0 +1,47 @@ + +#ifndef __TIMER_H__ +#define __TIMER_H__ + +#include +#include +#include "cuda.h" + +class GPUTimer { + cudaEvent_t start, stop; + +public: + GPUTimer() + { + cudaEventCreate(&start); + cudaEventCreate(&stop); + } + ~GPUTimer() + { + cudaEventDestroy(start); + cudaEventDestroy(stop); + } + inline void Record() { cudaEventRecord(start); } + inline void Elapsed(float& time_elapsed) + { + cudaEventRecord(stop); + cudaEventSynchronize(stop); + cudaEventElapsedTime(&time_elapsed, start, stop); + } +}; + +class CPUTimer { + std::chrono::high_resolution_clock::time_point start; + +public: + CPUTimer() : start(std::chrono::high_resolution_clock::now()) {} + inline void Reset() { start = std::chrono::high_resolution_clock::now(); } + inline float Elapsed() + { + auto temp = start; + start = std::chrono::high_resolution_clock::now(); + return (float)(std::chrono::duration_cast(start - temp).count() / + 1e3); + } +}; + +#endif diff --git a/csrc/includes/context.h b/csrc/includes/context.h new file mode 100644 index 000000000000..1e4820177c5d --- /dev/null +++ b/csrc/includes/context.h @@ -0,0 +1,177 @@ +#pragma once + +#include +#include +#include +#include +#include +#include "cublas_v2.h" +#include "cuda.h" +#include "curand.h" +#include "gemm_test.h" + +#define WARP_SIZE 32 + +#define CUDA_CHECK(callstr) \ + { \ + cudaError_t error_code = callstr; \ + if (error_code != cudaSuccess) { \ + std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__; \ + assert(0); \ + } \ + } + +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); i += blockDim.x * gridDim.x) + +#define CUDA_2D_KERNEL_LOOP(i, n, j, m) \ + for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); i += blockDim.x * gridDim.x) \ + for (size_t j = blockIdx.y * blockDim.y + threadIdx.y; j < (m); j += blockDim.y * gridDim.y) + +#define DS_CUDA_NUM_THREADS 512 +#define DS_MAXIMUM_NUM_BLOCKS 4096 + +inline int DS_GET_BLOCKS(const int N) +{ + return std::max( + std::min((N + DS_CUDA_NUM_THREADS - 1) / DS_CUDA_NUM_THREADS, DS_MAXIMUM_NUM_BLOCKS), + // Use at least 1 block, since CUDA does not allow empty block + 1); +} + +class Context { +public: + Context() : _workspace(nullptr), _seed(42), _curr_offset(0) + { + curandCreateGenerator(&_gen, CURAND_RNG_PSEUDO_DEFAULT); + curandSetPseudoRandomGeneratorSeed(_gen, 123); + if (cublasCreate(&_cublasHandle) != CUBLAS_STATUS_SUCCESS) { + auto message = std::string("Fail to create cublas handle."); + std::cerr << message << std::endl; + throw std::runtime_error(message); + } + } + + virtual ~Context() + { + cublasDestroy(_cublasHandle); + cudaFree(_workspace); + } + + static Context& Instance() + { + static Context _ctx; + return _ctx; + } + + void GenWorkSpace(size_t size) + { + if (!_workspace) { + assert(_workspace == nullptr); + cudaMalloc(&_workspace, size); + } else if (_workSpaceSize != size) { + cudaFree(_workspace); + cudaMalloc(&_workspace, size); + } + + _workSpaceSize = size; + } + + void* GetWorkSpace() { return _workspace; } + + curandGenerator_t& GetRandGenerator() { return _gen; } + + cudaStream_t GetCurrentStream() + { + // get current pytorch stream. + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + return stream; + } + + cublasHandle_t GetCublasHandle() { return _cublasHandle; } + + std::pair IncrementOffset(uint64_t offset_inc) + { + uint64_t offset = _curr_offset; + _curr_offset += offset_inc; + return std::pair(_seed, offset); + } + + void SetSeed(uint64_t new_seed) { _seed = new_seed; } + + void TestGemmFP16(bool test_gemm, int batch_size, int seq_len, int head_num, int size_per_head) + { + // avoid rerun. + if (_gemm_algos.size() > 0) return; + + if (test_gemm) { + cublasHandle_t handle = GetCublasHandle(); + + std::unique_ptr> test_qkv_fw( + new GemmTest<__half>(batch_size * seq_len, // M + head_num * size_per_head, // N + head_num * size_per_head, // K + CUBLAS_OP_T, + CUBLAS_OP_N, + handle)); + + std::unique_ptr> test_inter( + new GemmTest<__half>(batch_size * seq_len, // M + 4 * head_num * size_per_head, // N + head_num * size_per_head, // K + CUBLAS_OP_T, + CUBLAS_OP_N, + handle)); + + std::unique_ptr> test_output( + new GemmTest<__half>(batch_size * seq_len, // M + head_num * size_per_head, // N + 4 * head_num * size_per_head, // K + CUBLAS_OP_T, + CUBLAS_OP_N, + handle)); + + std::unique_ptr> test_attn_scores( + new StridedGemmTest<__half>(batch_size * head_num, // batch + seq_len, // M + seq_len, // N + size_per_head, // K + CUBLAS_OP_T, + CUBLAS_OP_N, + handle)); + + std::unique_ptr> test_attn_context( + new StridedGemmTest<__half>(batch_size * head_num, // batch + size_per_head, // M + seq_len, // N + seq_len, // K + CUBLAS_OP_N, + CUBLAS_OP_N, + handle)); + + _gemm_algos.push_back(test_qkv_fw->TestAlgo(100)); + _gemm_algos.push_back(test_inter->TestAlgo(100)); + _gemm_algos.push_back(test_output->TestAlgo(100)); + _gemm_algos.push_back(test_attn_scores->TestAlgo(100)); + _gemm_algos.push_back(test_attn_context->TestAlgo(100)); + } else { + // Use default algo. + _gemm_algos.push_back(std::array({99, 99, 99})); + _gemm_algos.push_back(std::array({99, 99, 99})); + _gemm_algos.push_back(std::array({99, 99, 99})); + _gemm_algos.push_back(std::array({99, 99, 99})); + _gemm_algos.push_back(std::array({99, 99, 99})); + } + } + + const std::vector>& GetGemmAlgos() const { return _gemm_algos; } + +private: + curandGenerator_t _gen; + cublasHandle_t _cublasHandle; + void* _workspace; + uint64_t _seed; + uint64_t _curr_offset; + size_t _workSpaceSize; + std::vector> _gemm_algos; +}; diff --git a/csrc/includes/cublas_wrappers.h b/csrc/includes/cublas_wrappers.h new file mode 100644 index 000000000000..19d726c3bcd3 --- /dev/null +++ b/csrc/includes/cublas_wrappers.h @@ -0,0 +1,69 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +int cublas_gemm_ex(cublasHandle_t handle, + cublasOperation_t transa, + cublasOperation_t transb, + int m, + int n, + int k, + const float* alpha, + const float* beta, + const float* A, + const float* B, + float* C, + cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT); + +int cublas_gemm_ex(cublasHandle_t handle, + cublasOperation_t transa, + cublasOperation_t transb, + int m, + int n, + int k, + const float* alpha, + const float* beta, + const __half* A, + const __half* B, + __half* C, + cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP); + +int cublas_strided_batched_gemm(cublasHandle_t handle, + int m, + int n, + int k, + const float* alpha, + const float* beta, + const float* A, + const float* B, + float* C, + cublasOperation_t op_A, + cublasOperation_t op_B, + int stride_A, + int stride_B, + int stride_C, + int batch, + cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT); + +int cublas_strided_batched_gemm(cublasHandle_t handle, + int m, + int n, + int k, + const float* alpha, + const float* beta, + const __half* A, + const __half* B, + __half* C, + cublasOperation_t op_A, + cublasOperation_t op_B, + int stride_A, + int stride_B, + int stride_C, + int batch, + cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP); diff --git a/csrc/includes/custom_cuda_layers.h b/csrc/includes/custom_cuda_layers.h new file mode 100755 index 000000000000..d9d0a8a68b38 --- /dev/null +++ b/csrc/includes/custom_cuda_layers.h @@ -0,0 +1,266 @@ +#pragma once + +#include +#include +#include +#include + +#include +#include + +#include "context.h" +#include "cublas_wrappers.h" + +#define MAX_THREADS 1024 +#define THREADS 256 + +#define MAX_THREAD_STRIDE 32 +#define TILE_DIM 32 + +// Maximum sequence-length support based on the number of threads (2048) allowed in each block and +// this MAX is 8K For higher sequence length we need to use higher Max, like for 64K : 32 +#define MAX_THREAD_ITERATIONS 8 // Maximum 8K +#define MAX_WARP_NUM 32 + +// Fused bias add with gelu activation +template +void launch_bias_gelu(const T* input, + const T* bias, + T* output, + int intermediate_size, + int batch_size, + int sequence_length, + cudaStream_t stream); + +template +void launch_gelu(const T* input, + T* output, + int intermediate_size, + int batch_size, + int sequence_length, + cudaStream_t stream); + +template +void launch_d_gelu(T* d_output, + const T* input, + const T* bias, + int intermediate_size, + int batch_size, + int sequence_length, + cudaStream_t stream); + +// Custom fused bias add with layer normalization +template +void launch_bias_residual_layer_norm(T* vals, + const T* residual, + const T* gamma, + const T* beta, + float epsilon, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream, + bool preLayerNorm, + bool training = false, + T* vars = nullptr, + T* means = nullptr, + T* vals_hat = nullptr); + +template +void launch_bias_residual_layer_norm(T* vals, + const T* residual, + const T* gamma, + const T* beta, + float epsilon, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream, + bool preLayerNorm, + bool training = false, + T* vars = nullptr, + T* vals_hat = nullptr, + bool save_vals = false); + +template +void launch_layerNorm_backward_fused_add(const T* out_grad1, + const T* out_grad2, + const T* X_data, + const T* vars, + const T* means, + const T* gamma, + T* gamma_grad, + T* betta_grad, + T* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2]); +template +void launch_layerNorm_backward_fused_add(const T* out_grad1, + const T* out_grad2, + const T* vals_hat, + const T* vars, + const T* gamma, + T* gamma_grad, + T* betta_grad, + T* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2], + bool invertible = false, + const T* betta = nullptr); + +template +void launch_layerNorm_backward(const T* out_grad, + const T* X_data, + const T* vars, + const T* means, + const T* gamma, + T* gamma_grad, + T* betta_grad, + T* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2]); + +template +void launch_layerNorm_backward(const T* out_grad, + const T* vals_hat, + const T* vars, + const T* gamma, + T* gamma_grad, + T* betta_grad, + T* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2], + bool invertible = false, + const T* betta = nullptr); + +template +void launch_layerNorm_backward_nreversible(const T* out_grad, + const T* vals, + const T* out_grad_trans, + const T* vals_trans, + const T* means, + const T* vars, + const T* gamma, + T* gamma_grad, + T* betta_grad, + T* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2]); + +template +void Transpose(const T* inp_mat, T* out_mat, int rows, int cols, cudaStream_t stream); + +template +void launch_attn_softmax_backward(T* out_grad, + const T* soft_inp, + int batch_size, + int heads, + int seq_length, + cudaStream_t stream); + +template +void launch_attn_softmax_backward_v2(T* out_grad, + const T* soft_inp, + int batch_size, + int heads, + int seq_length, + cudaStream_t stream); + +// Custom softmax with scaling and attention mask addition +template +void launch_attn_softmax(T* vals, + const T* attn_mask, + int batch_size, + int heads, + int sequence_length, + cudaStream_t stream); + +template +void launch_transform_0213(T* output, + const T* vals, + int batch_size, + int seq_length, + int hidden_dim, + int heads, + cudaStream_t stream); + +// Custom bias add +template +void launch_bias_add_transform_0213(T* outputs, + const T* vals, + const T* bias, + int batch_size, + int seq_length, + int hidden_dim, + int heads, + cudaStream_t stream, + int trans_count); + +// 4D transform [0, 1, 2, 3] -> [0, 2, 1, 3] +template +void launch_transform4d_0213(T* out, + const T* in, + int batch_size, + int heads, + int seq_length, + int hidden_dim, + cudaStream_t stream, + int trans_count); + +template +void launch_dropout(T* vals, + const T* bias, + uint8_t* mask, + int batch, + int dim, + float ratio, + cudaStream_t stream); + +template +void launch_dropout(T* vals_out, + const T* vals, + uint8_t* mask, + int total_count, + int dim, + float ratio, + cudaStream_t stream, + bool bwd = false); + +template +void launch_dropout(T* out, + const T* vals, + const T* residual, + const T* bias, + uint8_t* mask, + int batch, + int dim, + float ratio, + cudaStream_t stream); + +template +void launch_dropout_grad(T* vals, uint8_t* mask, int total_count, float ratio, cudaStream_t stream); + +template +void launch_dropout_grad(T* vals_out, + const T* vals, + uint8_t* mask, + int total_count, + float ratio, + cudaStream_t stream); + +template +void launch_fuse_transpose_bias_kernel(const T* inp, + T* out, + int rows, + int cols, + cudaStream_t stream); diff --git a/csrc/includes/dropout.h b/csrc/includes/dropout.h new file mode 100644 index 000000000000..090df3a0abf8 --- /dev/null +++ b/csrc/includes/dropout.h @@ -0,0 +1,76 @@ +#pragma once + +#include +#include +#include + +template +class Dropout { +public: + struct Config { + float ratio; + uint32_t batch, dim; + bool training; + + Config(float r, uint32_t batch, uint32_t dim) + : ratio(r), batch(batch), dim(dim), training(true) + { + } + + float RATIO() const { return training ? ratio : 0.0; } + }; + + Dropout(const Config& config) : _config(config), _mask(nullptr) {} + + virtual ~Dropout() {} + + void Forward(int bsz, T* out, const T* vals, cudaStream_t stream, bool bwd = false) + { + launch_dropout( + out, vals, _mask, bsz * _config.dim, _config.dim, _config.RATIO(), stream, bwd); + } + + void ForwardWithBias(int bsz, T* vals, const T* bias, cudaStream_t stream) + { + launch_dropout(vals, bias, _mask, bsz, _config.dim, _config.RATIO(), stream); + } + + void ForwardWithBias(int bsz, + T* out, + const T* vals, + const T* residual, + const T* bias, + cudaStream_t stream) + { + launch_dropout( + out, vals, residual, bias, _mask, bsz, _config.dim, _config.RATIO(), stream); + } + + void Backward(int bsz, T* d_vals, cudaStream_t stream) + { + launch_dropout_grad(d_vals, _mask, bsz * _config.dim, _config.RATIO(), stream); + } + + void Backward(int bsz, T* d_vals_out, const T* d_vals, cudaStream_t stream) + { + launch_dropout_grad( + d_vals_out, d_vals, _mask, bsz * _config.dim, _config.RATIO(), stream); + } + + bool HasDropout() const { return _config.RATIO() > 0.0; } + + void SetTrainingMode(bool training) { _config.training = training; } + + void SetMask(uint8_t* mask) + { + if (!mask) { throw std::runtime_error("Dropout mask is null."); } + + _mask = mask; + } + + Config GetConfig() const { return _config; } + +private: + uint8_t* _mask; + Config _config; +}; diff --git a/csrc/includes/ds_transformer_cuda.h b/csrc/includes/ds_transformer_cuda.h new file mode 100755 index 000000000000..896dce8c26db --- /dev/null +++ b/csrc/includes/ds_transformer_cuda.h @@ -0,0 +1,174 @@ +#pragma once + +#include +#include +#include +#include +#include "cublas_v2.h" +#include "cuda.h" +#include "dropout.h" +#include "feed_forward.h" +#include "gelu.h" +#include "general_kernels.h" +#include "normalize_layer.h" +#include "softmax.h" +#include "strided_batch_gemm.h" + +struct BertGemmAlgos { + int m_gemm_qkv_algo; + int m_gemm_inter_algo; + int m_gemm_output_algo; + int m_gemm_batch1_algo; + int m_gemm_batch2_algo; + + BertGemmAlgos() + : m_gemm_qkv_algo(-1), + m_gemm_inter_algo(-1), + m_gemm_output_algo(-1), + m_gemm_batch1_algo(-1), + m_gemm_batch2_algo(-1) + { + } +}; + +template +class BertTransformerLayer { +public: + BertTransformerLayer(int layer_id, + int batch_size, + int hidden_size, + int num_heads, + int intermediate_size, + int seq_length, + float attn_dropout_ratio, + float hidden_output_dropout_ratio, + bool pre_or_postLayerNorm, + const std::vector>& gemm_algos, + bool attn_dropout_checkpoint, + bool normalize_invertible, + bool gelu_checkpoint, + bool stochastic_mode); + + virtual ~BertTransformerLayer(); + + void Forward(int bsz, + const T* input_ptr, + const T* input_mask_ptr, + const T* attn_qkvw_ptr, + const T* attn_qkvb_ptr, + const T* attn_ow_ptr, + const T* attn_ob_ptr, + const T* attn_nw_ptr, + const T* attn_nb_ptr, + const T* inter_w_ptr, + const T* inter_b_ptr, + const T* output_w_ptr, + const T* output_b_ptr, + const T* norm_w_ptr, + const T* norm_b_ptr, + T* out_ptr, + T* inp_norm_ptr, + T* q_tf_ptr, + T* k_tf_ptr, + T* v_tf_ptr, + T* softmax_output_ptr, + T* ctx_bufB_ptr, + T* attn_o_inp_ptr, + T* add_res_ptr, + T* ff1_inp_ptr, + T* gelu_inp_ptr, + T* ff2_inp_ptr); + + void Backward(int bsz, + const T* grad_output_ptr, + const T* input_ptr, + const T* output_ptr, + const T* inp_norm_ptr, + const T* q_tf_ptr, + const T* k_tf_ptr, + const T* v_tf_ptr, + const T* softmax_output_ptr, + const T* ctx_bufB_ptr, + const T* attn_o_inp_ptr, + const T* add_res_ptr, + const T* ff1_inp_ptr, + const T* gelu_inp_ptr, + const T* ff2_inp_ptr, + const T* input_mask_ptr, + const T* attn_qkvw_ptr, + const T* attn_ow_ptr, + const T* attn_nw_ptr, + const T* attn_nb_ptr, + const T* inter_w_ptr, + const T* inter_b_ptr, + const T* output_w_ptr, + const T* norm_w_ptr, + const T* norm_b_ptr, + + T* grad_input_ptr, + T* grad_attn_qkvw_ptr, + T* grad_attn_qkvb_ptr, + T* grad_attn_ow_ptr, + T* grad_attn_ob_ptr, + T* grad_attn_nw_ptr, + T* grad_attn_nb_ptr, + T* grad_inter_w_ptr, + T* grad_inter_b_ptr, + T* grad_output_w_ptr, + T* grad_output_b_ptr, + T* grad_norm_w_ptr, + T* grad_norm_b_ptr); + + void SetIntermediateBuffers(uint8_t* attn_prob_dropout_mask_ptr, + uint8_t* attn_output_dropout_mask_ptr, + uint8_t* layer_output_dropout_mask_ptr); + + inline int GetBatchSize() const { return _batch_size; } + inline int GetNumHeads() const { return _heads; } + inline int GetSeqLength() const { return _seq_length; } + inline int GetHiddenSize() const { return _hidden_size; } + void SetTrainingMode(bool training); + +private: + void Initialize(); + size_t getWorkspaceSize(int maxBatchSize) const; + + // Params + int _layer_id; + int _batch_size; + int _hidden_size; + int _heads; + int _size_per_head; + int _intermediate_size; + int _seq_length; + + bool _pre_or_postLayerNorm; + + cublasHandle_t _cublasHandle; + cudaStream_t _stream; + + // layers + FeedForward _qkv_linear; + FeedForward _attn_out_linear; + Normalize_Layer _norm_layer2; + Normalize_Layer _norm_layer3; + Normalize_Layer* _last_normalize; + FeedForward _ff1, _ff2; + Softmax _softmax; + Gelu _gelu; + Dropout _attn_prob_dropout; + Dropout _attn_output_dropout; + Dropout _layer_output_dropout; + StridedBatchGemm _attn_scores; + StridedBatchGemm _attn_context; + + bool _training; + + // Memory saving flags + bool _attn_dropout_checkpoint; + bool _normalize_invertible; + bool _gelu_checkpoint; + + // High Performace flags + bool _stochastic_mode; +}; diff --git a/csrc/includes/feed_forward.h b/csrc/includes/feed_forward.h new file mode 100644 index 000000000000..7b7379d9b998 --- /dev/null +++ b/csrc/includes/feed_forward.h @@ -0,0 +1,93 @@ +#ifndef __FEEDFORWARD_H__ +#define __FEEDFORWARD_H__ + +#include +#include +#include +#include "custom_cuda_layers.h" + +template +class FeedForward { +public: + struct Config { + int batchSize, outputSize; + int inputSize; + std::array gemm_algos; + Config(int batch, int outputs, int inputs, const std::array& algos) + : batchSize(batch), outputSize(outputs), inputSize(inputs), gemm_algos(algos) + { + } + }; + + FeedForward(Config config) : config_(config) {} + + ~FeedForward() {} + + void Forward(int bsz, + const T* input_ptr, + const T* weights, + T* out, + cublasHandle_t& _cublasHandle) + { + float alpha = T(1.); + float beta = T(0.); + + cublas_gemm_ex(_cublasHandle, + CUBLAS_OP_T, + CUBLAS_OP_N, + config_.outputSize, + bsz, + config_.inputSize, + &alpha, + &beta, + weights, + input_ptr, + out, + cublasGemmAlgo_t(config_.gemm_algos[0])); + } + void Backward(int bsz, + const T* out_grad, + const T* input_ptr, + const T* weights, + T* weights_grad, + T* bias_grad, + cublasHandle_t& _cublasHandle, + cudaStream_t& stream, + T* inp_grad_out = nullptr, + T* out_grad_trans_out = nullptr) + { + float alpha = (T)1.0, beta = (T)0.0; + cublas_gemm_ex(_cublasHandle, + CUBLAS_OP_N, + CUBLAS_OP_T, + config_.inputSize, + config_.outputSize, + bsz, + &alpha, + &beta, + input_ptr, + out_grad, + weights_grad, + cublasGemmAlgo_t(config_.gemm_algos[1])); + + cublas_gemm_ex(_cublasHandle, + CUBLAS_OP_N, + CUBLAS_OP_N, + config_.inputSize, + bsz, + config_.outputSize, + &alpha, + &beta, + weights, + out_grad, + inp_grad_out, + cublasGemmAlgo_t(config_.gemm_algos[2])); + + launch_fuse_transpose_bias_kernel(out_grad, bias_grad, bsz, config_.outputSize, stream); + } + +private: + Config config_; +}; + +#endif diff --git a/csrc/includes/gelu.h b/csrc/includes/gelu.h new file mode 100644 index 000000000000..247bfb273de0 --- /dev/null +++ b/csrc/includes/gelu.h @@ -0,0 +1,43 @@ +#pragma once + +#include +#include +#include +#include "custom_cuda_layers.h" + +template +class Gelu { +public: + struct Config { + uint32_t batch_size; + uint32_t seq_length; + uint32_t intermediate_size; + Config(uint32_t batch, uint32_t seq, uint32_t inter_size) + : batch_size(batch), seq_length(seq), intermediate_size(inter_size) + { + } + }; + + Gelu(const Config& config) : _config(config) {} + + virtual ~Gelu() {} + + void ForwardWithBiasAdd(int bsz, + const T* input_buf, + const T* bias, + T* output, + cudaStream_t stream) + { + launch_bias_gelu( + input_buf, bias, output, _config.intermediate_size, bsz, _config.seq_length, stream); + } + + void Backward(int bsz, T* d_output, const T* input_buf, const T* bias, cudaStream_t stream) + { + launch_d_gelu( + d_output, input_buf, bias, _config.intermediate_size, bsz, _config.seq_length, stream); + } + +private: + Config _config; +}; diff --git a/csrc/includes/gemm_test.h b/csrc/includes/gemm_test.h new file mode 100644 index 000000000000..ff06f884351c --- /dev/null +++ b/csrc/includes/gemm_test.h @@ -0,0 +1,293 @@ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include "StopWatch.h" +#include "cublas_wrappers.h" + +template +void check(T result, char const* const func, const char* const file, int const line) +{ + if (result) { + std::cout << (std::string("CUDA runtime error: ") + +file + ":" + std::to_string(line) + + " \n"); + } +} + +#define check_cuda_error(val) check((val), #val, __FILE__, __LINE__) + +template +class GemmTest { +public: + GemmTest(int m, int n, int k, cublasOperation_t ta, cublasOperation_t tb, cublasHandle_t h) + : M(m), N(n), K(k), transa(ta), transb(tb), handle(h) + { + check_cuda_error(cudaMalloc((void**)&A, sizeof(T) * M * K)); + check_cuda_error(cudaMalloc((void**)&B, sizeof(T) * K * N)); + check_cuda_error(cudaMalloc((void**)&C, sizeof(T) * M * N)); + } + + ~GemmTest() + { + check_cuda_error(cudaFree(A)); + check_cuda_error(cudaFree(B)); + check_cuda_error(cudaFree(C)); + } + + std::array TestAlgo(int loops) + { + float alpha = (T)1.0f; + float beta = (T)0.0f; + + int algo_fw = Run(loops, [=](int algo) { + cublas_gemm_ex(handle, + CUBLAS_OP_T, + CUBLAS_OP_N, + N, + M, + K, + &alpha, + &beta, + B, + A, + C, + static_cast(algo)); + }); + + int algo_bw1 = Run(loops, [=](int algo) { + cublas_gemm_ex(handle, + CUBLAS_OP_N, + CUBLAS_OP_T, + K, + N, + M, + &alpha, + &beta, + A, + C, + B, + static_cast(algo)); + }); + + int algo_bw2 = Run(loops, [=](int algo) { + cublas_gemm_ex(handle, + CUBLAS_OP_N, + CUBLAS_OP_N, + K, + M, + N, + &alpha, + &beta, + B, + C, + A, + static_cast(algo)); + }); + + return std::array({algo_fw, algo_bw1, algo_bw2}); + } + + template + int Run(int loops, Func f) + { + float fast_latency = std::numeric_limits::max(); + int fast_algo = 0; + + for (int algo = (int)CUBLAS_GEMM_DEFAULT_TENSOR_OP; + algo <= (int)CUBLAS_GEMM_ALGO15_TENSOR_OP; + algo++) { + int warm_up = 5; + for (int i = 0; i < warm_up; ++i) f(algo); + + cudaDeviceSynchronize(); + Stopwatch timer; + timer.Restart(); + + for (int i = 0; i < loops; ++i) f(algo); + + cudaDeviceSynchronize(); + timer.Stop(); + + float avg_latency = (float)timer.GetTimeInSeconds() * 1000 / loops; + + printf("algo-%d: %.3fms\n", algo, avg_latency); + + if (avg_latency < fast_latency) { + fast_latency = avg_latency; + fast_algo = algo; + } + } + + printf("fast_algo %d: %.3f ms\n", fast_algo, fast_latency); + + return fast_algo; + } + +private: + int M, N, K; + cublasHandle_t handle; + cublasOperation_t transa, transb; + T *A, *B, *C; +}; + +template +class StridedGemmTest { +public: + StridedGemmTest(int b, + int m, + int n, + int k, + cublasOperation_t ta, + cublasOperation_t tb, + cublasHandle_t h) + : bsz(b), M(m), N(n), K(k), transa(ta), transb(tb), handle(h) + { + check_cuda_error(cudaMalloc((void**)&A, sizeof(T) * M * K * bsz)); + check_cuda_error(cudaMalloc((void**)&B, sizeof(T) * K * N * bsz)); + check_cuda_error(cudaMalloc((void**)&C, sizeof(T) * M * N * bsz)); + } + + ~StridedGemmTest() + { + check_cuda_error(cudaFree(A)); + check_cuda_error(cudaFree(B)); + check_cuda_error(cudaFree(C)); + } + + std::array TestAlgo(int loops) + { + float alpha = (T)1.0f; + float beta = (T)0.0f; + + int algo_fw = Run(loops, [=](int algo) { + int stride_a = M * K; + int stride_b = N * K; + int stride_c = M * N; + + cublas_strided_batched_gemm(handle, + M, + N, + K, + &alpha, + &beta, + A, + B, + C, + transa, + transb, + stride_a, + stride_b, + stride_c, + bsz, + static_cast(algo)); + }); + + int algo_bw1 = Run(loops, [=](int algo) { + int mb = (transa == CUBLAS_OP_T ? K : M); + int kb = (transa == CUBLAS_OP_T ? M : K); + + int stride_a = mb * N; + int stride_b = N * kb; + int stride_c = M * K; + + // B need to transpose. + cublasOperation_t op_b = (transb == CUBLAS_OP_T ? CUBLAS_OP_N : CUBLAS_OP_T); + + // Calculate d_A. + cublas_strided_batched_gemm(handle, + mb, + kb, + N, + &alpha, + &beta, + (transa == CUBLAS_OP_T ? B : C), + (transa == CUBLAS_OP_T ? C : B), + A, + CUBLAS_OP_N, + op_b, + stride_a, + stride_b, + stride_c, + bsz, + static_cast(algo)); + }); + + int algo_bw2 = Run(loops, [=](int algo) { + // A need to transpose. + cublasOperation_t op_a = (transa == CUBLAS_OP_T ? CUBLAS_OP_N : CUBLAS_OP_T); + + int stride_a = M * K; + int stride_b = M * N; + int stride_c = N * K; + + // Calculate d_B. + cublas_strided_batched_gemm(handle, + K, + N, + M, + &alpha, + &beta, + A, + C, + B, + op_a, + CUBLAS_OP_N, + stride_a, + stride_b, + stride_c, + bsz, + static_cast(algo)); + }); + + return std::array({algo_fw, algo_bw1, algo_bw2}); + } + + template + int Run(int loops, Func f) + { + float fast_latency = std::numeric_limits::max(); + int fast_algo = 0; + + for (int algo = (int)CUBLAS_GEMM_DEFAULT_TENSOR_OP; + algo <= (int)CUBLAS_GEMM_ALGO15_TENSOR_OP; + algo++) { + int warm_up = 5; + for (int i = 0; i < warm_up; ++i) f(algo); + + cudaDeviceSynchronize(); + Stopwatch timer; + timer.Restart(); + + for (int i = 0; i < loops; ++i) f(algo); + + cudaDeviceSynchronize(); + timer.Stop(); + + float avg_latency = (float)timer.GetTimeInSeconds() * 1000 / loops; + + printf("algo-%d: %.3fms\n", algo, avg_latency); + + if (avg_latency < fast_latency) { + fast_latency = avg_latency; + fast_algo = algo; + } + } + + printf("fast_algo %d: %.3f ms\n", fast_algo, fast_latency); + + return fast_algo; + } + +private: + int bsz, M, N, K; + cublasHandle_t handle; + cublasOperation_t transa, transb; + T *A, *B, *C; +}; diff --git a/csrc/includes/general_kernels.h b/csrc/includes/general_kernels.h new file mode 100644 index 000000000000..588cf2aaa048 --- /dev/null +++ b/csrc/includes/general_kernels.h @@ -0,0 +1,47 @@ +#include +#include +#include +#include + +#include +#include + +#include "context.h" +#include "cublas_wrappers.h" + +#define THREADS 256 +#define TILE_DIM 32 + +#define minus_infinity -1 * std::numeric_limits::infinity() + +#define FINAL_MASK 0xffffffff + +template +void launch_fused_add2(T* out, + const T* inp1, + const T* inp2, + int batch_size, + int seq_length, + int hidden_size, + cudaStream_t& stream); + +template +void launch_fused_add4(T* out, + const T* inp1, + const T* inp2, + const T* inp3, + const T* inp4, + int batch_size, + int seq_length, + int hidden_size, + cudaStream_t& stream); + +template +void launch_fused_add3(T* out, + const T* inp1, + const T* inp2, + const T* inp3, + int batch_size, + int seq_length, + int hidden_size, + cudaStream_t& stream); diff --git a/csrc/includes/normalize_layer.h b/csrc/includes/normalize_layer.h new file mode 100644 index 000000000000..37ee752c88b5 --- /dev/null +++ b/csrc/includes/normalize_layer.h @@ -0,0 +1,227 @@ +#pragma once + +#include +#include +#include +#include +#include "custom_cuda_layers.h" + +using namespace std; + +template +class Normalize_Layer { +public: + struct Config { + uint32_t batchSize; + uint32_t seqLength; + uint32_t hiddenDim; + float epsilon; + bool training, save_vals; + bool allocateGrad; + bool useMean; + Config(uint32_t batch, + uint32_t seq, + uint32_t h, + bool training, + bool save_vals = true, + bool allocateGrad = true, + bool useMean = true) + : batchSize(batch), + seqLength(seq), + hiddenDim(h), + epsilon(1e-12), + training(training), + save_vals(save_vals), + allocateGrad(allocateGrad), + useMean(useMean) + { + } + }; + + Normalize_Layer(Config config) : config_(config), vars(nullptr), vals_hat(nullptr) + { + if (config_.training) { + cudaMalloc((void**)&vars, config_.batchSize * config_.seqLength * sizeof(T)); + + if (config_.useMean) + cudaMalloc((void**)&means, config_.batchSize * config_.seqLength * sizeof(T)); + + if (config_.save_vals) + cudaMalloc((void**)&vals_hat, + config_.batchSize * config_.seqLength * config_.hiddenDim * sizeof(T)); + + if (config_.allocateGrad) + cudaMalloc((void**)&inp_grad, + config_.batchSize * config_.seqLength * config_.hiddenDim * sizeof(T)); + } + } + + ~Normalize_Layer() + { + if (config_.training) { + cudaFree(vars); + if (config_.useMean) cudaFree(means); + if (config_.save_vals) cudaFree(vals_hat); + if (config_.allocateGrad) cudaFree(inp_grad); + } + } + + void ForwardCheckpoint(int bsz, + T* vals, + const T* residual, + const T* gamma, + const T* betta, + cudaStream_t& stream, + bool preLayerNorm = false) + { + launch_bias_residual_layer_norm(vals, + residual, + gamma, + betta, + config_.epsilon, + bsz, + config_.seqLength, + config_.hiddenDim, + stream, + preLayerNorm, + config_.training, + vars, + means, + vals_hat); + } + + void Forward(int bsz, + T* vals, + const T* residual, + const T* gamma, + const T* betta, + cudaStream_t& stream, + bool preLayerNorm = false) + { + launch_bias_residual_layer_norm(vals, + residual, + gamma, + betta, + config_.epsilon, + bsz, + config_.seqLength, + config_.hiddenDim, + stream, + preLayerNorm, + config_.training, + vars, + vals_hat, + config_.save_vals); + } + + void Backward(int bsz, + const T* out_grad, + const T* gamma, + T* gamma_grad, + T* betta_grad, + cudaStream_t stream[2], + T* inp_grad_out = nullptr, + const T* norm_in = nullptr) + { + launch_layerNorm_backward(out_grad, + norm_in, + vars, + means, + gamma, + gamma_grad, + betta_grad, + (config_.allocateGrad ? inp_grad : inp_grad_out), + bsz, + config_.seqLength, + config_.hiddenDim, + stream); + } + + void Backward(int bsz, + const T* out_grad, + const T* gamma, + const T* betta, + T* gamma_grad, + T* betta_grad, + cudaStream_t stream[2], + T* inp_grad_out = nullptr, + const T* norm_out = nullptr) + { + launch_layerNorm_backward(out_grad, + (config_.save_vals ? vals_hat : norm_out), + vars, + gamma, + gamma_grad, + betta_grad, + (config_.allocateGrad ? inp_grad : inp_grad_out), + bsz, + config_.seqLength, + config_.hiddenDim, + stream, + config_.save_vals, + betta); + } + + void BackwardFusedAdd(int bsz, + const T* out_grad1, + const T* out_grad2, + const T* gamma, + T* gamma_grad, + T* betta_grad, + cudaStream_t stream[2], + T* inp_grad_out = nullptr, + const T* norm_in = nullptr) + { + launch_layerNorm_backward_fused_add(out_grad1, + out_grad2, + norm_in, + vars, + means, + gamma, + gamma_grad, + betta_grad, + (config_.allocateGrad ? inp_grad : inp_grad_out), + bsz, + config_.seqLength, + config_.hiddenDim, + stream); + } + + void BackwardFusedAdd(int bsz, + const T* out_grad1, + const T* out_grad2, + const T* gamma, + const T* betta, + T* gamma_grad, + T* betta_grad, + cudaStream_t stream[2], + T* inp_grad_out = nullptr, + const T* norm_out = nullptr) + { + launch_layerNorm_backward_fused_add(out_grad1, + out_grad2, + (config_.save_vals ? vals_hat : norm_out), + vars, + gamma, + gamma_grad, + betta_grad, + (config_.allocateGrad ? inp_grad : inp_grad_out), + bsz, + config_.seqLength, + config_.hiddenDim, + stream, + config_.save_vals, + betta); + } + + inline T* GetInputGrad() const { return inp_grad; } + + inline bool UseMean() const { return config_.useMean; } + +private: + Config config_; + T* vars; + T* means; + T* vals_hat; + T* inp_grad; +}; diff --git a/csrc/includes/softmax.h b/csrc/includes/softmax.h new file mode 100644 index 000000000000..2a18daee0b78 --- /dev/null +++ b/csrc/includes/softmax.h @@ -0,0 +1,58 @@ +#pragma once + +#include +#include +#include +#include "custom_cuda_layers.h" + +#include + +using namespace std; + +template +class Softmax { +public: + struct Config { + size_t batchSize; + size_t heads; + size_t seq_length; + size_t prob_depth; + float temprature; + bool mem_alloc; + Config(size_t batch, size_t h, size_t seq, int prob_size = 0, bool mem_alloc = false) + : batchSize(batch), + heads(h), + seq_length(seq), + prob_depth(prob_size), + temprature(1.0), + mem_alloc(mem_alloc) + { + } + }; + + Softmax(Config config) : config_(config) {} + + ~Softmax() {} + + void Forward(int bsz, T* vals, const T* attn_mask, cudaStream_t& stream) + { + launch_attn_softmax(vals, attn_mask, bsz, config_.heads, config_.seq_length, stream); + } + + void Backward(int bsz, T* out_grad, const T* soft_out, cudaStream_t stream) + { + launch_attn_softmax_backward_v2( + out_grad, soft_out, bsz, config_.heads, config_.seq_length, stream); + } + + inline int GetProbDepth() const { return config_.prob_depth; } + + inline int GetBatchSize() const { return config_.batchSize; } + + inline int GetNumHeads() const { return config_.heads; } + + inline int GetSeqLength() const { return config_.seq_length; } + +private: + Config config_; +}; diff --git a/csrc/includes/strided_batch_gemm.h b/csrc/includes/strided_batch_gemm.h new file mode 100644 index 000000000000..8c43608e2ecf --- /dev/null +++ b/csrc/includes/strided_batch_gemm.h @@ -0,0 +1,170 @@ +#pragma once + +#include +#include +#include + +template +class StridedBatchGemm { +public: + struct Config { + int batch_size; + int m; + int n; + int k; + float alpha; + float beta; + cublasOperation_t op_A; + cublasOperation_t op_B; + std::array gemm_algos; + + Config(int batch, + int mm, + int nn, + int kk, + float param_alpha, + float param_beta, + cublasOperation_t opA, + cublasOperation_t opB, + const std::array& algos) + : batch_size(batch), + m(mm), + n(nn), + k(kk), + alpha(param_alpha), + beta(param_beta), + op_A(opA), + op_B(opB), + gemm_algos(algos) + { + } + }; + + StridedBatchGemm(const Config& config) : _config(config) {} + + virtual ~StridedBatchGemm() {} + + void Forward(int bsz, T* output, const T* _buffer_a, const T* _buffer_b, cublasHandle_t handle) + { + int stride_a = _config.m * _config.k; + int stride_b = _config.n * _config.k; + int stride_c = _config.m * _config.n; + + cublas_strided_batched_gemm(handle, + _config.m, + _config.n, + _config.k, + &_config.alpha, + &_config.beta, + _buffer_a, + _buffer_b, + output, + _config.op_A, + _config.op_B, + stride_a, + stride_b, + stride_c, + bsz, + cublasGemmAlgo_t(_config.gemm_algos[0])); + } + + void ForwardPlusSave(T* output, const T* _buffer_a, const T* _buffer_b, cublasHandle_t handle) + { + int stride_a = _config.m * _config.k; + int stride_b = _config.n * _config.k; + int stride_c = _config.m * _config.n; + + cublas_strided_batched_gemm(handle, + _config.m, + _config.n, + _config.k, + &_config.alpha, + &_config.beta, + _buffer_a, + _buffer_b, + output, + _config.op_A, + _config.op_B, + stride_a, + stride_b, + stride_c, + _config.batch_size, + cublasGemmAlgo_t(_config.gemm_algos[0])); + + k_buf = _buffer_a; + q_buf = _buffer_b; + } + + void Backward(int bsz, + const T* d_output, + const T* _buffer_a, + const T* _buffer_b, + cublasHandle_t handle, + T* inpGradA = nullptr, + T* inpGradB = nullptr) + { + int mb = (_config.op_A == CUBLAS_OP_T ? _config.k : _config.m); + int kb = (_config.op_A == CUBLAS_OP_T ? _config.m : _config.k); + + int stride_a = mb * _config.n; + int stride_b = _config.n * kb; + int stride_c = _config.m * _config.k; + + // B need to transpose. + cublasOperation_t op_b = (_config.op_B == CUBLAS_OP_T ? CUBLAS_OP_N : CUBLAS_OP_T); + + // Calculate d_A. + cublas_strided_batched_gemm(handle, + mb, + kb, + _config.n, + &_config.alpha, + &_config.beta, + (_config.op_A == CUBLAS_OP_T ? _buffer_b : d_output), + (_config.op_A == CUBLAS_OP_T ? d_output : _buffer_b), + inpGradA, + CUBLAS_OP_N, + op_b, + stride_a, + stride_b, + stride_c, + bsz, + cublasGemmAlgo_t(_config.gemm_algos[1])); + + // A need to transpose. + cublasOperation_t op_a = (_config.op_A == CUBLAS_OP_T ? CUBLAS_OP_N : CUBLAS_OP_T); + + stride_a = _config.m * _config.k; + stride_b = _config.m * _config.n; + stride_c = _config.n * _config.k; + + // Calculate d_B. + cublas_strided_batched_gemm(handle, + _config.k, + _config.n, + _config.m, + &_config.alpha, + &_config.beta, + _buffer_a, + d_output, + inpGradB, + op_a, + CUBLAS_OP_N, + stride_a, + stride_b, + stride_c, + bsz, + cublasGemmAlgo_t(_config.gemm_algos[2])); + } + + inline int GetN() const { return _config.k; } + + inline const T* GetBufferA() const { return k_buf; } + + inline const T* GetBufferB() const { return q_buf; } + +private: + Config _config; + const T* q_buf; + const T* k_buf; +}; diff --git a/csrc/includes/type_shim.h b/csrc/includes/type_shim.h new file mode 100644 index 000000000000..3c6b9c5ae98e --- /dev/null +++ b/csrc/includes/type_shim.h @@ -0,0 +1,109 @@ +/* Taken from NVIDIA/apex commit 855808f3fc268e9715d613f3c2e56469d8c986d8 */ +#include + +// Forward/backward compatiblity hack around +// https://github.com/pytorch/pytorch/commit/3aeb78079bcd68282fe9117088e138b77318e288 +// pending more future-proof guidance from upstream. +// struct TypeShim +// { +// const at::Type& payload; +// TypeShim(const at::Type& type) : payload(type) {} +// // Enable trivial conversion to a const at::Type& for pre-3aeb78 +// operator const at::Type&(){ return payload; }; +// // Enable dispatch switch statements to take *this directly for post-3aeb78 +// //operator at::ScalarType(){ return payload.; }; +// }; + +#define DISPATCH_FLOAT_AND_HALF(TYPE, LEVEL, NAME, ...) \ + switch (TYPE) { \ + case at::ScalarType::Float: { \ + using scalar_t_##LEVEL = float; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Half: { \ + using scalar_t_##LEVEL = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + default: AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } + +#define DISPATCH_DOUBLE_FLOAT_AND_HALF(TYPE, LEVEL, NAME, ...) \ + switch (TYPE) { \ + case at::ScalarType::Double: { \ + using scalar_t_##LEVEL = double; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Float: { \ + using scalar_t_##LEVEL = float; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Half: { \ + using scalar_t_##LEVEL = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + default: AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } + +#define DISPATCH_DOUBLE_AND_FLOAT(TYPE, LEVEL, NAME, ...) \ + switch (TYPE) { \ + case at::ScalarType::Double: { \ + using scalar_t_##LEVEL = double; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Float: { \ + using scalar_t_##LEVEL = float; \ + __VA_ARGS__; \ + break; \ + } \ + default: AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } + +template +__device__ __forceinline__ T +reduce_block_into_lanes(T* x, + T val, + int lanes = 1, + bool share_result = false) // lanes is intended to be <= 32. +{ + int tid = threadIdx.x + threadIdx.y * blockDim.x; + int blockSize = blockDim.x * blockDim.y; // blockSize is intended to be a multiple of 32. + + if (blockSize >= 64) { + x[tid] = val; + __syncthreads(); + } + +#pragma unroll + for (int i = (blockSize >> 1); i >= 64; i >>= 1) { + if (tid < i) x[tid] = x[tid] + x[tid + i]; + __syncthreads(); + } + + T final; + + if (tid < 32) { + if (blockSize >= 64) + final = x[tid] + x[tid + 32]; + else + final = val; + // __SYNCWARP(); + +#pragma unroll + for (int i = 16; i >= lanes; i >>= 1) + final = final + __shfl_down_sync(0xffffffff, final, i); + } + + if (share_result) { + if (tid < lanes) x[tid] = final; // EpilogueOp + // Make sure the smem result is visible to all warps. + __syncthreads(); + } + + return final; +} diff --git a/csrc/lamb/fused_lamb_cuda.cpp b/csrc/lamb/fused_lamb_cuda.cpp new file mode 100644 index 000000000000..efa4f18d4d94 --- /dev/null +++ b/csrc/lamb/fused_lamb_cuda.cpp @@ -0,0 +1,109 @@ +/* Copyright 2019 The Microsoft DeepSpeed Team */ +#include + +// CUDA forward declaration +void fused_lamb_cuda(at::Tensor& p, + at::Tensor& p_copy, + at::Tensor& m, + at::Tensor& v, + at::Tensor& g, + float lr, + float beta1, + float beta2, + float max_coeff, + float min_coeff, + float eps, + float grad_scale, + int step, + int mode, + int bias_correction, + float decay, + at::Tensor& w_l2_i, + at::Tensor& u_l2_i, + at::Tensor& lamb_coeff_val); + +#define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) + +// C++ interface +at::Tensor lamb(at::Tensor& p, + at::Tensor& p_copy, + at::Tensor& m, + at::Tensor& v, + at::Tensor& g, + float lr, + float beta1, + float beta2, + float max_coeff, + float min_coeff, + float eps, + float grad_scale, + int step, + int mode, + int bias_correction, + float decay) +{ + CHECK_INPUT(p); + if (p_copy.numel() > 0) CHECK_INPUT(p_copy); + CHECK_INPUT(m); + CHECK_INPUT(v); + CHECK_INPUT(g); + int64_t num_elem = p.numel(); + AT_ASSERTM(m.numel() == num_elem, "number of elements in m and p tensors should be equal"); + AT_ASSERTM(v.numel() == num_elem, "number of elements in v and p tensors should be equal"); + AT_ASSERTM(g.numel() == num_elem, "number of elements in g and p tensors should be equal"); + AT_ASSERTM( + p_copy.numel() == num_elem || p_copy.numel() == 0, + "number of elements in p_copy and p tensors should be equal, or p_copy should be empty"); + + // intermediate for weight L2 reduction + // make sure that the threads per block is at least 512 during the kernel launch otherwise the + // behavious is unexpected + at::Tensor w_l2_i = at::empty( + {512}, + p.options().dtype(p.type().scalarType() == at::ScalarType::Half ? at::ScalarType::Float + : p.type().scalarType())); + + // intermediate for update L2 reduction + // make sure that the threads per block is at least 512 during the kernel launch otherwise the + // behavious is unexpected + at::Tensor u_l2_i = at::empty( + {512}, + p.options().dtype(p.type().scalarType() == at::ScalarType::Half ? at::ScalarType::Float + : p.type().scalarType())); + + at::Tensor lamb_coeff_val = at::empty( + {1}, + p.options().dtype(p.type().scalarType() == at::ScalarType::Half ? at::ScalarType::Float + : p.type().scalarType())); + + fused_lamb_cuda(p, + p_copy, + m, + v, + g, + lr, + beta1, + beta2, + max_coeff, + min_coeff, + eps, + grad_scale, + step, + mode, + bias_correction, + decay, + w_l2_i, + u_l2_i, + lamb_coeff_val); + + return lamb_coeff_val; +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("lamb", &lamb, "Adam optimized CUDA implementation with LAMB."); +} diff --git a/csrc/lamb/fused_lamb_cuda_kernel.cu b/csrc/lamb/fused_lamb_cuda_kernel.cu new file mode 100644 index 000000000000..c94e9bb9562c --- /dev/null +++ b/csrc/lamb/fused_lamb_cuda_kernel.cu @@ -0,0 +1,469 @@ +/* Copyright 2019 The Microsoft DeepSpeed Team */ +#include +#include +#include +#include +#include "ATen/ATen.h" +#include "ATen/TensorUtils.h" +#include "ATen/cuda/CUDAContext.h" +#include "ATen/cuda/detail/IndexUtils.cuh" +//#include "ATen/Type.h" +#include +#include "ATen/AccumulateType.h" + +#include + +//#include +#include +#include +#include + +namespace cg = cooperative_groups; + +// Utility class used to avoid linker errors with extern +// unsized shared memory arrays with templated type +namespace { +// This is the un-specialized struct. Note that we prevent instantiation of this +// struct by putting an undefined symbol in the function body so it won't compile. +template +struct SharedMemory { + // Ensure that we won't compile any un-specialized types + __device__ inline operator T*() + { + extern __device__ void error(void); + error(); + return NULL; + } +}; + +template <> +struct SharedMemory { + __device__ inline operator float*() + { + extern __shared__ float s_float[]; + return s_float; + } +}; + +template <> +struct SharedMemory { + __device__ inline operator double*() + { + extern __shared__ double s_double[]; + return s_double; + } +}; +} // namespace + +#include "type_shim.h" + +typedef enum { + ADAM_MODE_0 = 0, // eps under square root + ADAM_MODE_1 = 1 // eps outside square root +} adamMode_t; + +// s_a and s_b are in shared memory +// g_a and g_b are in shared memory +template +__device__ void reduce_block_in_shared_memory(T* s_a, T* s_b, T* g_a, T* g_b) +{ + // Handle to thread block group + cg::thread_block cta = cg::this_thread_block(); + + // perform block reduction in shared memory, + unsigned int tid = cta.thread_rank(); + + T a_sum = s_a[tid]; + T b_sum = s_b[tid]; + + cg::sync(cta); + + // do reduction in shared mem + if ((blockSize >= 512) && (tid < 256)) { + s_a[tid] = a_sum = a_sum + s_a[tid + 256]; + s_b[tid] = b_sum = b_sum + s_b[tid + 256]; + } + + cg::sync(cta); + + if ((blockSize >= 256) && (tid < 128)) { + s_a[tid] = a_sum = a_sum + s_a[tid + 128]; + s_b[tid] = b_sum = b_sum + s_b[tid + 128]; + } + + cg::sync(cta); + + if ((blockSize >= 128) && (tid < 64)) { + s_a[tid] = a_sum = a_sum + s_a[tid + 64]; + s_b[tid] = b_sum = b_sum + s_b[tid + 64]; + } + + cg::sync(cta); + +#if (__CUDA_ARCH__ >= 300) + if (tid < 32) { + cg::coalesced_group active = cg::coalesced_threads(); + + // Fetch final intermediate sum from 2nd warp + if (blockSize >= 64) { + a_sum = a_sum + s_a[tid + 32]; + b_sum = b_sum + s_b[tid + 32]; + } + + // Reduce final warp using shuffle + for (int offset = warpSize / 2; offset > 0; offset /= 2) { + a_sum += active.shfl_down(a_sum, offset); + b_sum += active.shfl_down(b_sum, offset); + } + } +#else + if ((blockSize >= 64) && (tid < 32)) { + s_a[tid] = a_sum = a_sum + s_a[tid + 32]; + s_b[tid] = b_sum = b_sum + s_b[tid + 32]; + } + + cg::sync(cta); + + if ((blockSize >= 32) && (tid < 16)) { + s_a[tid] = a_sum = a_sum + s_a[tid + 16]; + s_b[tid] = b_sum = b_sum + s_b[tid + 16]; + } + + cg::sync(cta); + + if ((blockSize >= 16) && (tid < 8)) { + s_a[tid] = a_sum = a_sum + s_a[tid + 8]; + s_b[tid] = b_sum = b_sum + s_b[tid + 8]; + } + + cg::sync(cta); + + if ((blockSize >= 8) && (tid < 4)) { + s_a[tid] = a_sum = a_sum + s_a[tid + 4]; + s_b[tid] = b_sum = b_sum + s_b[tid + 4]; + } + + cg::sync(cta); + + if ((blockSize >= 4) && (tid < 2)) { + s_a[tid] = a_sum = a_sum + s_a[tid + 2]; + s_b[tid] = b_sum = b_sum + s_b[tid + 2]; + } + + cg::sync(cta); + + if ((blockSize >= 2) && (tid < 1)) { + s_a[tid] = a_sum = a_sum + s_a[tid + 1]; + s_b[tid] = b_sum = b_sum + s_b[tid + 1]; + } + + cg::sync(cta); + +#endif + + // write result for this block to global mem + if (tid == 0) { + g_a[blockIdx.x] = (T)a_sum; + g_b[blockIdx.x] = (T)b_sum; + } +} + +template +__device__ void reduce_two_vectors_in_register(T a, T b, T* g_a, T* g_b) +{ + const int threadIdInBlock = cg::this_thread_block().thread_rank(); + + T* s_a = SharedMemory(); + T* s_b = SharedMemory() + cg::this_thread_block().size(); + + s_a[threadIdInBlock] = a; + s_b[threadIdInBlock] = b; + + reduce_block_in_shared_memory(s_a, s_b, g_a, g_b); +} + +template +__global__ void lamb_cuda_kernel_part1( + T* __restrict__ p, + GRAD_T* __restrict__ p_copy, // For mixed precision training, pass NULL if not needed + T* __restrict__ m, + T* __restrict__ v, + const GRAD_T* __restrict__ g, + const float b1, + const float b2, + const float eps, + const float grad_scale, + const float step_size, + const size_t tsize, + adamMode_t mode, + const float decay, + T* __restrict__ w_l2_i, + T* __restrict__ u_l2_i) +{ + // Assuming 2D grids and 2D blocks + const int blockId = gridDim.x * blockIdx.y + blockIdx.x; + const int threadsPerBlock = blockDim.x * blockDim.y; + const int threadIdInBlock = cg::this_thread_block().thread_rank(); + const int i = (blockId * threadsPerBlock + threadIdInBlock); + const int totThreads = gridDim.x * gridDim.y * threadsPerBlock; + + T reg_w = 0; + T reg_u = 0; + + for (int j = i; j < tsize; j += totThreads) { + T scaled_grad = g[j] / grad_scale; + T pj = p[j]; + m[j] = b1 * m[j] + (1 - b1) * scaled_grad; + v[j] = b2 * v[j] + (1 - b2) * scaled_grad * scaled_grad; + float denom; + if (mode == ADAM_MODE_0) + denom = sqrtf(v[j] + eps); + else // Mode 1 + denom = sqrtf(v[j]) + eps; + T update = (m[j] / denom) + (decay * p[j]); + + reg_u += update * update; + reg_w += pj * pj; + } + + reduce_two_vectors_in_register(reg_w, reg_u, w_l2_i, u_l2_i); +} + +template +__global__ void lamb_cuda_kernel_part2(const size_t tsize, T* __restrict__ g_a, T* __restrict__ g_b) +{ + T* s_a = SharedMemory(); + T* s_b = SharedMemory() + cg::this_thread_block().size(); + + const int threadIdInBlock = cg::this_thread_block().thread_rank(); + + s_a[threadIdInBlock] = g_a[threadIdInBlock]; + s_b[threadIdInBlock] = g_b[threadIdInBlock]; + + if (threadIdInBlock >= tsize) { + s_a[threadIdInBlock] = 0.0; + s_b[threadIdInBlock] = 0.0; + } + + reduce_block_in_shared_memory(s_a, s_b, g_a, g_b); +} + +template +__global__ void lamb_cuda_kernel_part3( + T* __restrict__ p, + GRAD_T* __restrict__ p_copy, // For mixed precision training, pass NULL if not needed + T* __restrict__ m, + T* __restrict__ v, + const GRAD_T* __restrict__ g, + const float b1, + const float b2, + const float max_coeff, + const float min_coeff, + const float eps, + const float grad_scale, + const float step_size, + const size_t tsize, + adamMode_t mode, + const float decay, + T* __restrict__ w_l2_i, + T* __restrict__ u_l2_i, + T* __restrict__ lamb_coeff_val) +{ + // Assuming 2D grids and 2D blocks + const int blockId = gridDim.x * blockIdx.y + blockIdx.x; + const int threadsPerBlock = blockDim.x * blockDim.y; + const int threadIdInBlock = cg::this_thread_block().thread_rank(); + const int i = (blockId * threadsPerBlock + threadIdInBlock); + const int totThreads = gridDim.x * gridDim.y * threadsPerBlock; + + T reg_w = sqrtf(w_l2_i[0]); + T reg_u = sqrtf(u_l2_i[0]); + + float lamb_coeff = 1.0; + + if (reg_w != 0 and reg_u != 0) { + lamb_coeff = reg_w / reg_u; + if (lamb_coeff > max_coeff) { lamb_coeff = max_coeff; } + if (lamb_coeff < min_coeff) { lamb_coeff = min_coeff; } + } + + if (blockId == 0 and threadIdInBlock == 0) { + lamb_coeff_val[0] = lamb_coeff; + // printf("Cuda Lamb Coeff is %.6f \n",lamb_coeff); + } + + for (int j = i; j < tsize; j += totThreads) { + T pj = (float)p[j]; + T mj = m[j]; + T vj = v[j]; + float denom; + if (mode == ADAM_MODE_0) + denom = sqrtf(vj + eps); + else // Mode 1 + denom = sqrtf(vj) + eps; + T update = (mj / denom) + (decay * pj); + + pj = pj - (step_size * lamb_coeff * update); + p[j] = pj; + if (p_copy != NULL) p_copy[j] = (GRAD_T)pj; + } +} + +void fused_lamb_cuda(at::Tensor& p, + at::Tensor& p_copy, + at::Tensor& m, + at::Tensor& v, + at::Tensor& g, + float lr, + float beta1, + float beta2, + float max_coeff, + float min_coeff, + float eps, + float grad_scale, + int step, + int mode, + int bias_correction, + float decay, + at::Tensor& w_l2_i, + at::Tensor& u_l2_i, + at::Tensor& lamb_coeff) +{ + // using namespace at; + + // Get tensor size + int tsize = p.numel(); + // Determine #threads and #blocks + const int threadsPerBlock = 512; + int num_blocks = (tsize + threadsPerBlock - 1) / threadsPerBlock; + if (num_blocks > 512) num_blocks = 512; + + int smemsize = 0; + + if (p.type().scalarType() == at::ScalarType::Double) + smemsize = 2 * threadsPerBlock * sizeof(double); + else + smemsize = 2 * threadsPerBlock * sizeof(float); + + const dim3 blocks(num_blocks); + const dim3 threads(threadsPerBlock); + + AT_ASSERTM(at::cuda::detail::canUse32BitIndexMath(p), + "parameter tensor is too large to be indexed with int32"); + // Constants + float step_size = 0; + if (bias_correction == 1) { + const float bias_correction1 = 1 - std::pow(beta1, step); + const float bias_correction2 = 1 - std::pow(beta2, step); + step_size = lr * std::sqrt(bias_correction2) / bias_correction1; + } else { + step_size = lr; + } + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + if (g.type().scalarType() == at::ScalarType::Half) { + // all other values should be fp32 for half gradients + AT_ASSERTM(p.type().scalarType() == at::ScalarType::Float, + "expected parameter to be of float type"); + // dispatch is done on the gradient type + using namespace at; // prevents "toString is undefined" errors + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + g.scalar_type(), "lamb_cuda_kernel", ([&] { + using accscalar_t = at::acc_type; + + lamb_cuda_kernel_part1 + <<>>( + p.data(), + p_copy.numel() ? p_copy.data() : NULL, + m.data(), + v.data(), + g.data(), + beta1, + beta2, + eps, + grad_scale, + step_size, + tsize, + (adamMode_t)mode, + decay, + w_l2_i.data(), + u_l2_i.data()); + + lamb_cuda_kernel_part2 + <<<1, threadsPerBlock, smemsize, stream>>>( + num_blocks, w_l2_i.data(), u_l2_i.data()); + + lamb_cuda_kernel_part3 + <<>>( + p.data(), + p_copy.numel() ? p_copy.data() : NULL, + m.data(), + v.data(), + g.data(), + beta1, + beta2, + max_coeff, + min_coeff, + eps, + grad_scale, + step_size, + tsize, + (adamMode_t)mode, + decay, + w_l2_i.data(), + u_l2_i.data(), + lamb_coeff.data()); + })); + } else { + using namespace at; + AT_DISPATCH_FLOATING_TYPES( + g.scalar_type(), "lamb_cuda_kernel", ([&] { + lamb_cuda_kernel_part1 + <<>>( + p.data(), + NULL, // don't output p_copy for fp32, it's wasted write + m.data(), + v.data(), + g.data(), + beta1, + beta2, + eps, + grad_scale, + step_size, + tsize, + (adamMode_t)mode, + decay, + w_l2_i.data(), + u_l2_i.data()); + + lamb_cuda_kernel_part2 + <<<1, threadsPerBlock, smemsize, stream>>>( + num_blocks, w_l2_i.data(), u_l2_i.data()); + + lamb_cuda_kernel_part3 + <<>>( + p.data(), + NULL, // don't output p_copy for fp32, it's wasted write + m.data(), + v.data(), + g.data(), + beta1, + beta2, + max_coeff, + min_coeff, + eps, + grad_scale, + step_size, + tsize, + (adamMode_t)mode, + decay, + w_l2_i.data(), + u_l2_i.data(), + lamb_coeff.data()); + })); + } + THCudaCheck(cudaGetLastError()); +} + +// template __device__ void reduce_two_vectors_in_register(float a, float b, float* g_a, +// float* g_b, cg::grid_group &cgg); diff --git a/csrc/transformer/cublas_wrappers.cu b/csrc/transformer/cublas_wrappers.cu new file mode 100644 index 000000000000..7b0016bcae5e --- /dev/null +++ b/csrc/transformer/cublas_wrappers.cu @@ -0,0 +1,178 @@ +#include "cublas_wrappers.h" + +int cublas_gemm_ex(cublasHandle_t handle, + cublasOperation_t transa, + cublasOperation_t transb, + int m, + int n, + int k, + const float* alpha, + const float* beta, + const float* A, + const float* B, + float* C, + cublasGemmAlgo_t algo) +{ + cublasStatus_t status = cublasGemmEx(handle, + transa, + transb, + m, + n, + k, + (const void*)alpha, + (const void*)A, + CUDA_R_32F, + (transa == CUBLAS_OP_N) ? m : k, + (const void*)B, + CUDA_R_32F, + (transb == CUBLAS_OP_N) ? k : n, + (const void*)beta, + C, + CUDA_R_32F, + m, + CUDA_R_32F, + algo); + + if (status != CUBLAS_STATUS_SUCCESS) { + fprintf(stderr, "!!!! kernel execution error.\n"); + return EXIT_FAILURE; + } + return 0; +} + +int cublas_gemm_ex(cublasHandle_t handle, + cublasOperation_t transa, + cublasOperation_t transb, + int m, + int n, + int k, + const float* alpha, + const float* beta, + const __half* A, + const __half* B, + __half* C, + cublasGemmAlgo_t algo) +{ + cublasStatus_t status = cublasGemmEx(handle, + transa, + transb, + m, + n, + k, + (const void*)alpha, + (const void*)A, + CUDA_R_16F, + (transa == CUBLAS_OP_N) ? m : k, + (const void*)B, + CUDA_R_16F, + (transb == CUBLAS_OP_N) ? k : n, + (const void*)beta, + (void*)C, + CUDA_R_16F, + m, + CUDA_R_32F, + algo); + + if (status != CUBLAS_STATUS_SUCCESS) { + fprintf(stderr, "!!!! kernel execution error.\n"); + return EXIT_FAILURE; + } + return 0; +} + +int cublas_strided_batched_gemm(cublasHandle_t handle, + int m, + int n, + int k, + const float* alpha, + const float* beta, + const float* A, + const float* B, + float* C, + cublasOperation_t op_A, + cublasOperation_t op_B, + int stride_A, + int stride_B, + int stride_C, + int batch, + cublasGemmAlgo_t algo) +{ + cublasStatus_t status = cublasGemmStridedBatchedEx(handle, + op_A, + op_B, + m, + n, + k, + alpha, + A, + CUDA_R_32F, + (op_A == CUBLAS_OP_N) ? m : k, + stride_A, + B, + CUDA_R_32F, + (op_B == CUBLAS_OP_N) ? k : n, + stride_B, + beta, + C, + CUDA_R_32F, + m, + stride_C, + batch, + CUDA_R_32F, + algo); + + if (status != CUBLAS_STATUS_SUCCESS) { + fprintf(stderr, "!!!! kernel execution error.\n"); + return EXIT_FAILURE; + } + return 0; +} + +int cublas_strided_batched_gemm(cublasHandle_t handle, + int m, + int n, + int k, + const float* alpha, + const float* beta, + const __half* A, + const __half* B, + __half* C, + cublasOperation_t op_A, + cublasOperation_t op_B, + int stride_A, + int stride_B, + int stride_C, + int batch, + cublasGemmAlgo_t algo) +{ + cublasStatus_t status = cublasGemmStridedBatchedEx(handle, + op_A, + op_B, + m, + n, + k, + alpha, + A, + CUDA_R_16F, + (op_A == CUBLAS_OP_N) ? m : k, + stride_A, + B, + CUDA_R_16F, + (op_B == CUBLAS_OP_N) ? k : n, + stride_B, + beta, + C, + CUDA_R_16F, + m, + stride_C, + batch, + CUDA_R_32F, + algo); + + if (status != CUBLAS_STATUS_SUCCESS) { + fprintf(stderr, "!!!! kernel execution error.\n"); + return EXIT_FAILURE; + } + + return 0; +} diff --git a/csrc/transformer/dropout_kernels.cu b/csrc/transformer/dropout_kernels.cu new file mode 100644 index 000000000000..faf39b44eb91 --- /dev/null +++ b/csrc/transformer/dropout_kernels.cu @@ -0,0 +1,720 @@ +#include "custom_cuda_layers.h" + +__global__ void dropout_kernel(const int N, + const float ratio, + float* out, + const float* Xdata, + uint8_t* mask, + std::pair seed) +{ + const float scale = 1. / (1. - ratio); + int idx = blockIdx.x * blockDim.x + threadIdx.x; + + curandStatePhilox4_32_10_t state; + curand_init(seed.first, idx, seed.second, &state); + + CUDA_1D_KERNEL_LOOP(j, N / 4) + { + float4 rand = curand_uniform4(&state); + uint8_t m[4]; + + m[0] = (uint8_t)(rand.x > ratio); + m[1] = (uint8_t)(rand.y > ratio); + m[2] = (uint8_t)(rand.z > ratio); + m[3] = (uint8_t)(rand.w > ratio); + + int i = j * 4; + + mask[i] = (uint8_t)m[0]; + mask[i + 1] = (uint8_t)m[1]; + mask[i + 2] = (uint8_t)m[2]; + mask[i + 3] = (uint8_t)m[3]; + + out[i] = Xdata[i] * scale * m[0]; + out[i + 1] = Xdata[i + 1] * scale * m[1]; + out[i + 2] = Xdata[i + 2] * scale * m[2]; + out[i + 3] = Xdata[i + 3] * scale * m[3]; + } +} + +__global__ void dropout_kernel(const int N, + const float ratio, + __half* out, + const __half* Xdata, + uint8_t* mask, + std::pair seed) +{ + const float scale = 1. / (1. - ratio); + + int idx = blockIdx.x * blockDim.x + threadIdx.x; + + curandStatePhilox4_32_10_t state; + curand_init(seed.first, idx, seed.second, &state); + +#ifdef __STOCHASTIC_MODE__ + + const __half2 h_scale = __float2half2_rn(scale); + const float2* x_cast = reinterpret_cast(Xdata); + float2* out_cast = reinterpret_cast(out); + uint32_t* mask_cast = reinterpret_cast(mask); + + uint32_t m_32; + uint8_t* m = reinterpret_cast(&m_32); + + float2 result_f; + __half2* result_h = reinterpret_cast<__half2*>(&result_f); + __half2 mask_h[2]; + float2 mask_f[2]; + + CUDA_1D_KERNEL_LOOP(j, N / 4) + { + float2 x_f = x_cast[j]; + __half2* x_h = reinterpret_cast<__half2*>(&x_f); + + float4 rand = curand_uniform4(&state); + + m[0] = (uint8_t)(rand.x > ratio); + m[1] = (uint8_t)(rand.y > ratio); + m[2] = (uint8_t)(rand.z > ratio); + m[3] = (uint8_t)(rand.w > ratio); + + float* mask_f_data = &mask_f[0].x; +#pragma unroll + for (int i = 0; i < 4; i++) mask_f_data[i] = (float)(m[i]); + + mask_h[0] = __float22half2_rn(mask_f[0]); + mask_h[1] = __float22half2_rn(mask_f[1]); + + result_h[0] = x_h[0] * h_scale * mask_h[0]; + result_h[1] = x_h[1] * h_scale * mask_h[1]; + + out_cast[j] = result_f; + + mask_cast[j] = m_32; + } + +#else + + CUDA_1D_KERNEL_LOOP(j, N / 4) + { + int i = j * 4; + + const __half2* vals_half = reinterpret_cast(Xdata + i); + float2 vals_half_f[2]; + vals_half_f[0] = __half22float2(vals_half[0]); + vals_half_f[1] = __half22float2(vals_half[1]); + + uint8_t m[4]; + float4 rand = curand_uniform4(&state); + m[0] = (uint8_t)(rand.x > ratio); + m[1] = (uint8_t)(rand.y > ratio); + m[2] = (uint8_t)(rand.z > ratio); + m[3] = (uint8_t)(rand.w > ratio); + + out[i] = __float2half(vals_half_f[0].x * scale * m[0]); + out[i + 1] = __float2half(vals_half_f[0].y * scale * m[1]); + out[i + 2] = __float2half(vals_half_f[1].x * scale * m[2]); + out[i + 3] = __float2half(vals_half_f[1].y * scale * m[3]); + + mask[i] = m[0]; + mask[i + 1] = m[1]; + mask[i + 2] = m[2]; + mask[i + 3] = m[3]; + } + +#endif +} + +__global__ void dropout_kernel_bwd(const int N, + const float ratio, + const float* Xdata, + float* out, + uint8_t* mask, + std::pair seed) +{ + const float scale = 1. / (1. - ratio); + CUDA_1D_KERNEL_LOOP(j, N / 4) + { + int i = j * 4; + + out[i] = mask[i] ? Xdata[i] * scale : 0.0; + out[i + 1] = mask[i + 1] ? Xdata[i + 1] * scale : 0.0; + out[i + 2] = mask[i + 2] ? Xdata[i + 2] * scale : 0.0; + out[i + 3] = mask[i + 3] ? Xdata[i + 3] * scale : 0.0; + } +} + +__global__ void dropout_kernel_bwd(const int N, + const float ratio, + const __half* Xdata, + __half* out, + uint8_t* mask, + std::pair seed) +{ + const float scale = 1. / (1. - ratio); + +#ifdef __STOCHASTIC_MODE__ + + const __half2 h_scale = __float2half2_rn(scale); + + const float2* x_cast = reinterpret_cast(Xdata); + float2* out_cast = reinterpret_cast(out); + uint32_t* mask_cast = reinterpret_cast(mask); + + CUDA_1D_KERNEL_LOOP(j, N / 4) + { + float2 x_f = x_cast[j]; + __half2* x_h = reinterpret_cast<__half2*>(&x_f); + + uint8_t* m = reinterpret_cast(mask_cast + j); + __half2 mask_h[2]; + float2 mask_f[2]; + + float* mask_f_data = &mask_f[0].x; +#pragma unroll + for (int i = 0; i < 4; i++) mask_f_data[i] = (float)(m[i]); + +#pragma unroll + for (int i = 0; i < 2; i++) mask_h[i] = __float22half2_rn(mask_f[i]); + + float2 result_f; + __half2* result_h = reinterpret_cast<__half2*>(&result_f); + + result_h[0] = x_h[0] * h_scale * mask_h[0]; + result_h[1] = x_h[1] * h_scale * mask_h[1]; + + out_cast[j] = result_f; + } + +#else + + const __half h_scale = __float2half(scale); + const __half h_zero = __float2half(0.0); + + CUDA_1D_KERNEL_LOOP(j, N / 4) + { + int i = j * 4; + + const __half2* vals_half = reinterpret_cast(Xdata + i); + + uint8_t* m = mask + i; + + float2 vals_half_f[2]; + + vals_half_f[0] = __half22float2(vals_half[0]); + vals_half_f[1] = __half22float2(vals_half[1]); + + out[i] = __float2half(vals_half_f[0].x * scale * m[0]); + out[i + 1] = __float2half(vals_half_f[0].y * scale * m[1]); + out[i + 2] = __float2half(vals_half_f[1].x * scale * m[2]); + out[i + 3] = __float2half(vals_half_f[1].y * scale * m[3]); + } + +#endif +} + +template +void launch_dropout(T* out, + const T* vals, + uint8_t* mask, + int total_count, + int dim, + float ratio, + cudaStream_t stream, + bool bwd) +{ + dim3 grid_dim = DS_GET_BLOCKS(total_count / 4); + dim3 block_dim = DS_CUDA_NUM_THREADS; + + if (dim > 512) { + block_dim.x >>= 1; + grid_dim.x <<= 1; + } + uint64_t inc = total_count / grid_dim.x / block_dim.x; + std::pair seed = Context::Instance().IncrementOffset(inc); + if (bwd) + dropout_kernel_bwd<<>>( + total_count, ratio, vals, out, mask, seed); + else + dropout_kernel<<>>( + total_count, ratio, out, vals, mask, seed); +} + +template void launch_dropout(float* out, + const float* vals, + uint8_t* mask, + int total_count, + int dim, + float ratio, + cudaStream_t stream, + bool); +template void launch_dropout(__half* out, + const __half* vals, + uint8_t* mask, + int total_count, + int dim, + float ratio, + cudaStream_t stream, + bool); + +__global__ void dropout_grad_kernel(const int N, const float scale, float* Xdata, uint8_t* mask) +{ + CUDA_1D_KERNEL_LOOP(i, N) { Xdata[i] *= scale * mask[i]; } +} + +__global__ void dropout_grad_kernel(const int N, const float scale, __half* Xdata, uint8_t* mask) +{ +#ifdef __STOCHASTIC_MODE__ + + const __half2 h_scale = __float2half2_rn(scale); + float2* x_cast = reinterpret_cast(Xdata); + uint32_t* mask_cast = reinterpret_cast(mask); + + CUDA_1D_KERNEL_LOOP(j, N / 4) + { + uint8_t* m = reinterpret_cast(mask_cast + j); + __half2 mask_h[2]; + float2 mask_f[2]; + + float* mask_f_data = &mask_f[0].x; +#pragma unroll + for (int i = 0; i < 4; i++) *(mask_f_data++) = (float)(m[i]); + + mask_h[0] = __float22half2_rn(mask_f[0]); + mask_h[1] = __float22half2_rn(mask_f[1]); + + float2 x_data = x_cast[j]; + __half2* x_data_h = reinterpret_cast<__half2*>(&x_data); + + float2 result_f; + __half2* result_h = reinterpret_cast<__half2*>(&result_f); + + result_h[0] = x_data_h[0] * h_scale * mask_h[0]; + result_h[1] = x_data_h[1] * h_scale * mask_h[1]; + + x_cast[j] = result_f; + } + +#else + + CUDA_1D_KERNEL_LOOP(j, N / 2) + { + int i = j * 2; + Xdata[i] = (__half)((float)Xdata[i] * scale * mask[i]); + Xdata[i + 1] = (__half)((float)Xdata[i + 1] * scale * mask[i + 1]); + } + +#endif +} + +template +void launch_dropout_grad(T* vals, uint8_t* mask, int total_count, float ratio, cudaStream_t stream) +{ + const float scale = 1. / (1. - ratio); + dropout_grad_kernel<<>>( + total_count, scale, vals, mask); +} + +template void launch_dropout_grad(float* vals, + uint8_t* mask, + int total_count, + float ratio, + cudaStream_t stream); +template void launch_dropout_grad(__half* vals, + uint8_t* mask, + int total_count, + float ratio, + cudaStream_t stream); + +__global__ void dropout_grad_kernel(const int N, + const float scale, + const float* Xdata, + float* out, + uint8_t* mask) +{ + CUDA_1D_KERNEL_LOOP(i, N) { out[i] = Xdata[i] * scale * mask[i]; } +} + +__global__ void dropout_grad_kernel(const int N, + const float scale, + const __half* Xdata, + __half* out, + uint8_t* mask) +{ + CUDA_1D_KERNEL_LOOP(j, N / 2) + { + int i = j * 2; + out[i] = (__half)((float)Xdata[i] * scale * mask[i]); + out[i + 1] = (__half)((float)Xdata[i + 1] * scale * mask[i + 1]); + } +} + +template +void launch_dropout_grad(T* vals_out, + const T* vals, + uint8_t* mask, + int total_count, + float ratio, + cudaStream_t stream) +{ + const float scale = 1. / (1. - ratio); + dropout_grad_kernel<<>>( + total_count, scale, vals, vals_out, mask); +} +template void launch_dropout_grad(float*, + const float* vals, + uint8_t* mask, + int total_count, + float ratio, + cudaStream_t stream); +template void launch_dropout_grad(__half*, + const __half* vals, + uint8_t* mask, + int total_count, + float ratio, + cudaStream_t stream); + +__global__ void dropout_kernel(const int dim, + const float ratio, + const float* bias, + float* Xdata, + uint8_t* mask, + std::pair seed) +{ + const float scale = 1. / (1. - ratio); + int idx = blockIdx.x * blockDim.x + threadIdx.x; + int tid = threadIdx.x; + + curandStatePhilox4_32_10_t state; + curand_init(seed.first, idx, seed.second, &state); + + float4* Xdata_cast = reinterpret_cast(Xdata); + const float4* bias_cast = reinterpret_cast(bias); + + { + float4 rand = curand_uniform4(&state); + uint8_t m[4]; + + m[0] = (uint8_t)(rand.x > ratio); + m[1] = (uint8_t)(rand.y > ratio); + m[2] = (uint8_t)(rand.z > ratio); + m[3] = (uint8_t)(rand.w > ratio); + + int i = blockIdx.x * dim + tid * 4; + + float4 x_data = Xdata_cast[idx]; + float4 b_data = bias_cast[tid]; + + x_data.x += b_data.x; + x_data.y += b_data.y; + x_data.z += b_data.z; + x_data.w += b_data.w; + + x_data.x = x_data.x * scale * m[0]; + x_data.y = x_data.y * scale * m[1]; + x_data.z = x_data.z * scale * m[2]; + x_data.w = x_data.w * scale * m[3]; + + mask[i] = (uint8_t)m[0]; + mask[i + 1] = (uint8_t)m[1]; + mask[i + 2] = (uint8_t)m[2]; + mask[i + 3] = (uint8_t)m[3]; + + Xdata_cast[idx] = x_data; + } +} + +__global__ void dropout_kernel(const int dim, + const float ratio, + const __half* bias, + __half* Xdata, + uint8_t* mask, + std::pair seed) +{ + const float scale = 1. / (1. - ratio); + int idx = blockIdx.x * blockDim.x + threadIdx.x; + int tid = threadIdx.x; + + curandStatePhilox4_32_10_t state; + curand_init(seed.first, idx, seed.second, &state); + + float2* Xdata_cast = reinterpret_cast(Xdata); + const float2* bias_cast = reinterpret_cast(bias); + + { + int i = blockIdx.x * dim + tid * 4; + + float4 rand = curand_uniform4(&state); + + float2 data_f; + __half2* data_h = reinterpret_cast<__half2*>(&data_f); + + float2 bias_f; + __half2* bias_h = reinterpret_cast<__half2*>(&bias_f); + + data_f = Xdata_cast[idx]; + bias_f = bias_cast[tid]; + + float2 data_h_0 = __half22float2(data_h[0]); + float2 data_h_1 = __half22float2(data_h[1]); + + float2 bias_h_0 = __half22float2(bias_h[0]); + float2 bias_h_1 = __half22float2(bias_h[1]); + + data_h_0.x += bias_h_0.x; + data_h_0.y += bias_h_0.y; + data_h_1.x += bias_h_1.x; + data_h_1.y += bias_h_1.y; + + uint8_t m[4]; // = mask + i; + + m[0] = (uint8_t)(rand.x > ratio); + m[1] = (uint8_t)(rand.y > ratio); + m[2] = (uint8_t)(rand.z > ratio); + m[3] = (uint8_t)(rand.w > ratio); + + data_h_0.x = __float2half(data_h_0.x * scale * m[0]); + data_h_0.y = __float2half(data_h_0.y * scale * m[1]); + data_h_1.x = __float2half(data_h_1.x * scale * m[2]); + data_h_1.y = __float2half(data_h_1.y * scale * m[3]); + + float2 result_f; + __half2* result_h = reinterpret_cast<__half2*>(&result_f); + + result_h[0] = __float22half2_rn(data_h_0); + result_h[1] = __float22half2_rn(data_h_1); + + Xdata_cast[idx] = result_f; + + mask[i] = m[0]; + mask[i + 1] = m[1]; + mask[i + 2] = m[2]; + mask[i + 3] = m[3]; + } +} + +template +void launch_dropout(T* out, + const T* bias, + uint8_t* mask, + int batch, + int dim, + float ratio, + cudaStream_t stream) +{ + dim3 grid_dim(batch); // DS_GET_BLOCKS(total_count/4); + dim3 block_dim(dim / 4); // DS_CUDA_NUM_THREADS; + + uint64_t inc = (batch * dim) / grid_dim.x / block_dim.x; + std::pair seed = Context::Instance().IncrementOffset(inc); + + dropout_kernel<<>>(dim, ratio, bias, out, mask, seed); +} + +template void launch_dropout(float*, + const float* bias, + uint8_t* mask, + int batch, + int dim, + float ratio, + cudaStream_t stream); +template void launch_dropout(__half*, + const __half* bias, + uint8_t* mask, + int batch, + int dim, + float ratio, + cudaStream_t stream); + +__global__ void dropout_kernel(const int dim, + const float ratio, + const float* input, + const float* residual, + const float* bias, + float* out, + uint8_t* mask, + std::pair seed) +{ + const float scale = 1. / (1. - ratio); + int idx = blockIdx.x * blockDim.x + threadIdx.x; + int tid = threadIdx.x; + + curandStatePhilox4_32_10_t state; + curand_init(seed.first, idx, seed.second, &state); + + float4* out_cast = reinterpret_cast(out); + const float4* bias_cast = reinterpret_cast(bias); + const float4* residual_cast = reinterpret_cast(residual); + const float4* input_cast = reinterpret_cast(input); + + { + float4 rand = curand_uniform4(&state); + uint8_t m[4]; + m[0] = (uint8_t)(rand.x > ratio); + m[1] = (uint8_t)(rand.y > ratio); + m[2] = (uint8_t)(rand.z > ratio); + m[3] = (uint8_t)(rand.w > ratio); + + // int bid = k * blockDim.x + tid; + int i = blockIdx.x * dim + tid * 4; + + float4 out_data = out_cast[idx]; + float4 b_data = bias_cast[tid]; + float4 res_data = residual_cast[idx]; + float4 inp_data = input_cast[idx]; + + out_data.x = (b_data.x + inp_data.x); + out_data.y = (b_data.y + inp_data.y); + out_data.z = (b_data.z + inp_data.z); + out_data.w = (b_data.w + inp_data.w); + + out_data.x = out_data.x * scale * m[0]; + out_data.y = out_data.y * scale * m[1]; + out_data.z = out_data.z * scale * m[2]; + out_data.w = out_data.w * scale * m[3]; + + out_data.x += res_data.x; + out_data.y += res_data.y; + out_data.z += res_data.z; + out_data.w += res_data.w; + + mask[i] = m[0]; + mask[i + 1] = m[1]; + mask[i + 2] = m[2]; + mask[i + 3] = m[3]; + + out_cast[idx] = out_data; + } +} + +__global__ void dropout_kernel(const int dim, + const float ratio, + const __half* input, + const __half* residual, + const __half* bias, + __half* out, + uint8_t* mask, + std::pair seed) +{ + const float scale = 1. / (1. - ratio); + int idx = blockIdx.x * blockDim.x + threadIdx.x; + int tid = threadIdx.x; + + curandStatePhilox4_32_10_t state; + curand_init(seed.first, idx, seed.second, &state); + + float2* out_cast = reinterpret_cast(out); + const float2* bias_cast = reinterpret_cast(bias); + const float2* residual_cast = reinterpret_cast(residual); + const float2* input_cast = reinterpret_cast(input); + + { + int i = blockIdx.x * dim + tid * 4; + + float4 rand = curand_uniform4(&state); + + float2 data_f; + __half2* data_h = reinterpret_cast<__half2*>(&data_f); + + float2 bias_f; + __half2* bias_h = reinterpret_cast<__half2*>(&bias_f); + + float2 residual_f; + __half2* residual_h = reinterpret_cast<__half2*>(&residual_f); + + float2 input_f; + __half2* input_h = reinterpret_cast<__half2*>(&input_f); + + data_f = out_cast[idx]; + bias_f = bias_cast[tid]; + residual_f = residual_cast[idx]; + input_f = input_cast[idx]; + + float2 data_h_0 = __half22float2(data_h[0]); + float2 data_h_1 = __half22float2(data_h[1]); + + float2 bias_h_0 = __half22float2(bias_h[0]); + float2 bias_h_1 = __half22float2(bias_h[1]); + + float2 residual_h_0 = __half22float2(residual_h[0]); + float2 residual_h_1 = __half22float2(residual_h[1]); + + float2 input_h_0 = __half22float2(input_h[0]); + float2 input_h_1 = __half22float2(input_h[1]); + + data_h_0.x = (bias_h_0.x + input_h_0.x); + data_h_0.y = (bias_h_0.y + input_h_0.y); + data_h_1.x = (bias_h_1.x + input_h_1.x); + data_h_1.y = (bias_h_1.y + input_h_1.y); + + uint8_t m[4]; // = mask + i; + + m[0] = (uint8_t)(rand.x > ratio); + m[1] = (uint8_t)(rand.y > ratio); + m[2] = (uint8_t)(rand.z > ratio); + m[3] = (uint8_t)(rand.w > ratio); + + data_h_0.x = __float2half(data_h_0.x * scale * m[0]); + data_h_0.y = __float2half(data_h_0.y * scale * m[1]); + data_h_1.x = __float2half(data_h_1.x * scale * m[2]); + data_h_1.y = __float2half(data_h_1.y * scale * m[3]); + + data_h_0.x += residual_h_0.x; + data_h_0.y += residual_h_0.y; + data_h_1.x += residual_h_1.x; + data_h_1.y += residual_h_1.y; + + float2 result_f; + __half2* result_h = reinterpret_cast<__half2*>(&result_f); + + result_h[0] = __float22half2_rn(data_h_0); + result_h[1] = __float22half2_rn(data_h_1); + + out_cast[idx] = result_f; + + mask[i] = m[0]; + mask[i + 1] = m[1]; + mask[i + 2] = m[2]; + mask[i + 3] = m[3]; + } +} + +template +void launch_dropout(T* out, + const T* input, + const T* residual, + const T* bias, + uint8_t* mask, + int batch, + int dim, + float ratio, + cudaStream_t stream) +{ + dim3 grid_dim(batch); // DS_GET_BLOCKS(total_count/4); + dim3 block_dim(dim / 4); // DS_CUDA_NUM_THREADS; + + uint64_t inc = (batch * dim) / grid_dim.x / block_dim.x; + std::pair seed = Context::Instance().IncrementOffset(inc); + + dropout_kernel<<>>( + dim, ratio, input, residual, bias, out, mask, seed); +} + +template void launch_dropout(float*, + const float*, + const float* residual, + const float* bias, + uint8_t* mask, + int batch, + int dim, + float ratio, + cudaStream_t stream); +template void launch_dropout(__half*, + const __half*, + const __half* residual, + const __half* bias, + uint8_t* mask, + int batch, + int dim, + float ratio, + cudaStream_t stream); diff --git a/csrc/transformer/ds_transformer_cuda.cpp b/csrc/transformer/ds_transformer_cuda.cpp new file mode 100755 index 000000000000..269468bdfdb4 --- /dev/null +++ b/csrc/transformer/ds_transformer_cuda.cpp @@ -0,0 +1,974 @@ +#include + +#include +#include +#include +#include +#include +#include +#include "Timer.h" +#include "context.h" +#include "cublas_wrappers.h" +#include "custom_cuda_layers.h" +#include "ds_transformer_cuda.h" + +static std::unordered_map> s_transformer_layers; + +// C++ interface + +template +size_t get_workspace_size(int maxBatchSize, + int seq_len, + int hidden_size, + int heads, + bool training, + bool gelu_checkpoint) +{ + size_t workSpacesize = 4 * (size_t(maxBatchSize) * seq_len * hidden_size); + if (training) { + workSpacesize += (std::max((4 * size_t(maxBatchSize) * seq_len * hidden_size), + 2 * (size_t(maxBatchSize) * heads * seq_len * seq_len))); + if (gelu_checkpoint) workSpacesize += 2 * (size_t(maxBatchSize) * seq_len * hidden_size); + } + return workSpacesize * sizeof(T); +} + +// NOTE: AT_ASSERT has become AT_CHECK on master after 0.4. +#define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) + +template +BertTransformerLayer::BertTransformerLayer(int layer_id, + int batch_size, + int hidden_size, + int num_heads, + int intermediate_size, + int seq_length, + float attn_prob_dropout_ratio, + float hidden_output_dropout_ratio, + bool pre_or_postLayerNorm, + const std::vector>& gemm_algos, + bool attn_dropout_checkpoint, + bool normalize_invertible, + bool gelu_checkpoint, + bool stochastic_mode) + : _layer_id(layer_id), + _batch_size(batch_size), + _hidden_size(hidden_size), + _heads(num_heads), + _intermediate_size(intermediate_size), + _seq_length(seq_length), + _training(true), + _pre_or_postLayerNorm(pre_or_postLayerNorm), + _attn_dropout_checkpoint(attn_dropout_checkpoint), + _normalize_invertible(normalize_invertible), + _gelu_checkpoint(gelu_checkpoint), + _stochastic_mode(stochastic_mode), + _stream(Context::Instance().GetCurrentStream()), + _cublasHandle(Context::Instance().GetCublasHandle()), + _qkv_linear(typename FeedForward::Config(batch_size * seq_length, + 3 * hidden_size, + hidden_size, + gemm_algos[0])), + _attn_out_linear(typename FeedForward::Config(batch_size * seq_length, + hidden_size, + hidden_size, + gemm_algos[0])), + _norm_layer2(typename Normalize_Layer::Config(batch_size, + seq_length, + hidden_size, + true, + false, + false, + !normalize_invertible)), + _norm_layer3(typename Normalize_Layer::Config(batch_size, + seq_length, + hidden_size, + true, + false, + false, + !normalize_invertible)), + _ff1(typename FeedForward::Config(batch_size * seq_length, + 4 * hidden_size, + hidden_size, + gemm_algos[1])), + _ff2(typename FeedForward::Config(batch_size * seq_length, + hidden_size, + 4 * hidden_size, + gemm_algos[2])), + _softmax(typename Softmax::Config(batch_size, num_heads, seq_length)), + _gelu(typename Gelu::Config(_batch_size, _seq_length, _intermediate_size)), + _attn_prob_dropout(typename Dropout::Config(attn_prob_dropout_ratio, + _batch_size * _heads * _seq_length, + _seq_length)), + _attn_output_dropout(typename Dropout::Config(hidden_output_dropout_ratio, + _batch_size * _seq_length, + _hidden_size)), + _layer_output_dropout(typename Dropout::Config(hidden_output_dropout_ratio, + _batch_size * _seq_length, + _hidden_size)), + _attn_scores(typename StridedBatchGemm::Config(_batch_size * _heads, + _seq_length, + _seq_length, + _hidden_size / _heads, + (T(1.0) / T(sqrt(_hidden_size / _heads))), + T(0.0), + CUBLAS_OP_T, + CUBLAS_OP_N, + gemm_algos[3])), + _attn_context(typename StridedBatchGemm::Config(_batch_size * _heads, + _hidden_size / _heads, + _seq_length, + _seq_length, + T(1.0), + T(0.0), + CUBLAS_OP_N, + CUBLAS_OP_N, + gemm_algos[4])) +{ + assert(_hidden_size % _heads == 0); + assert(_seq_length <= 1024); + + Initialize(); +} + +template +BertTransformerLayer::~BertTransformerLayer() +{ +} + +template +void BertTransformerLayer::Initialize() +{ + Context::Instance().GenWorkSpace(get_workspace_size( + _batch_size, _seq_length, _hidden_size, _heads, _training, _gelu_checkpoint)); + + if (std::is_same::value) cublasSetMathMode(_cublasHandle, CUBLAS_TENSOR_OP_MATH); +} + +template +void BertTransformerLayer::Forward(int bsz, + const T* input_ptr, + const T* input_mask_ptr, + const T* attn_qkvw_ptr, + const T* attn_qkvb_ptr, + const T* attn_ow_ptr, + const T* attn_ob_ptr, + const T* attn_nw_ptr, + const T* attn_nb_ptr, + const T* inter_w_ptr, + const T* inter_b_ptr, + const T* output_w_ptr, + const T* output_b_ptr, + const T* norm_w_ptr, + const T* norm_b_ptr, + T* out_ptr, + T* inp_norm_ptr, + T* q_tf_ptr, + T* k_tf_ptr, + T* v_tf_ptr, + T* soft_out_ptr, + T* ctx_bufB_ptr, + T* attn_o_inp_ptr, + T* add_res_ptr, + T* ff1_inp_ptr, + T* gelu_inp_ptr, + T* ff2_inp_ptr) +{ + cublasSetStream(_cublasHandle, _stream); + + if (!_stochastic_mode) cudaStreamSynchronize(_stream); + + T* workspace = static_cast(Context::Instance().GetWorkSpace()); + size_t small_buf_size = bsz * _seq_length * _hidden_size; + T* buf_0 = workspace; + T* buf_1 = buf_0 + small_buf_size; + + if (_normalize_invertible) add_res_ptr = buf_1 + 3 * small_buf_size; + if (_attn_dropout_checkpoint) ctx_bufB_ptr = buf_1 + 4 * small_buf_size; + + if (_pre_or_postLayerNorm) { + if (_norm_layer3.UseMean()) + _norm_layer3.ForwardCheckpoint( + bsz, inp_norm_ptr, input_ptr, norm_w_ptr, norm_b_ptr, _stream, true); + + else + _norm_layer3.Forward( + bsz, inp_norm_ptr, input_ptr, norm_w_ptr, norm_b_ptr, _stream, true); + } + + int bsz_seq = bsz * _seq_length; + + if (_pre_or_postLayerNorm) + _qkv_linear.Forward(bsz_seq, inp_norm_ptr, attn_qkvw_ptr, buf_0, _cublasHandle); + else + _qkv_linear.Forward(bsz_seq, input_ptr, attn_qkvw_ptr, buf_0, _cublasHandle); + + launch_bias_add_transform_0213( + q_tf_ptr, buf_0, attn_qkvb_ptr, bsz, _seq_length, _hidden_size, _heads, _stream, 3); + + int bsz_heads = bsz * _heads; + + // attention scores + _attn_scores.Forward(bsz_heads, soft_out_ptr, k_tf_ptr, q_tf_ptr, _cublasHandle); + + // Softmax + Mask + _softmax.Forward(bsz, soft_out_ptr, input_mask_ptr, _stream); + + // attn prob dropout. + _attn_prob_dropout.Forward(bsz_heads * _seq_length, ctx_bufB_ptr, soft_out_ptr, _stream); + + // attention context + _attn_context.Forward(bsz_heads, buf_1, v_tf_ptr, ctx_bufB_ptr, _cublasHandle); + + launch_transform4d_0213( + attn_o_inp_ptr, buf_1, bsz, _heads, _seq_length, _hidden_size, _stream, 1); + + if (_pre_or_postLayerNorm) + _attn_out_linear.Forward(bsz_seq, attn_o_inp_ptr, attn_ow_ptr, buf_1, _cublasHandle); + else + _attn_out_linear.Forward(bsz_seq, attn_o_inp_ptr, attn_ow_ptr, ff1_inp_ptr, _cublasHandle); + + // attn output dropout. + if (_pre_or_postLayerNorm) + _attn_output_dropout.ForwardWithBias( + bsz_seq, add_res_ptr, buf_1, input_ptr, attn_ob_ptr, _stream); + else + _attn_output_dropout.ForwardWithBias( + bsz_seq, add_res_ptr, ff1_inp_ptr, input_ptr, attn_ob_ptr, _stream); + + if (_pre_or_postLayerNorm) { + if (_norm_layer2.UseMean()) + _norm_layer2.ForwardCheckpoint( + bsz, ff1_inp_ptr, add_res_ptr, attn_nw_ptr, attn_nb_ptr, _stream, true); + else + _norm_layer2.Forward( + bsz, ff1_inp_ptr, add_res_ptr, attn_nw_ptr, attn_nb_ptr, _stream, true); + } else { + if (_norm_layer2.UseMean()) + _norm_layer2.ForwardCheckpoint( + bsz, ff1_inp_ptr, add_res_ptr, attn_nw_ptr, attn_nb_ptr, _stream, true); + else + _norm_layer2.Forward( + bsz, ff1_inp_ptr, add_res_ptr, attn_nw_ptr, attn_nb_ptr, _stream, true); + } + + _ff1.Forward(bsz_seq, + ff1_inp_ptr, + inter_w_ptr, + (_gelu_checkpoint ? ff2_inp_ptr : gelu_inp_ptr), + _cublasHandle); + + _gelu.ForwardWithBiasAdd(bsz, + (_gelu_checkpoint ? ff2_inp_ptr : gelu_inp_ptr), + inter_b_ptr, + (_gelu_checkpoint ? ctx_bufB_ptr : ff2_inp_ptr), + _stream); + + _ff2.Forward(bsz_seq, + (_gelu_checkpoint ? ctx_bufB_ptr : ff2_inp_ptr), + output_w_ptr, + out_ptr, + _cublasHandle); + + // layer output dropout. + if (_pre_or_postLayerNorm) + _layer_output_dropout.ForwardWithBias( + bsz_seq, out_ptr, out_ptr, add_res_ptr, output_b_ptr, _stream); + else + _layer_output_dropout.ForwardWithBias( + bsz_seq, inp_norm_ptr, out_ptr, ff1_inp_ptr, output_b_ptr, _stream); + + if (!_pre_or_postLayerNorm) { + if (_norm_layer3.UseMean()) + _norm_layer3.ForwardCheckpoint( + bsz, out_ptr, inp_norm_ptr, norm_w_ptr, norm_b_ptr, _stream, true); + else + _norm_layer3.Forward(bsz, out_ptr, inp_norm_ptr, norm_w_ptr, norm_b_ptr, _stream, true); + } +} + +template +void BertTransformerLayer::Backward(int bsz, + const T* grad_output_ptr, + const T* input_ptr, + const T* output_ptr, + const T* inp_norm_ptr, + const T* q_tf_ptr, + const T* k_tf_ptr, + const T* v_tf_ptr, + const T* soft_out_ptr, + const T* ctx_bufB_ptr, + const T* attn_o_inp_ptr, + const T* add_res_ptr, + const T* ff1_inp_ptr, + const T* gelu_inp_ptr, + const T* ff2_inp_ptr, + const T* input_mask_ptr, + const T* attn_qkvw_ptr, + const T* attn_ow_ptr, + const T* attn_nw_ptr, + const T* attn_nb_ptr, + const T* inter_w_ptr, + const T* inter_b_ptr, + const T* output_w_ptr, + const T* norm_w_ptr, + const T* norm_b_ptr, + + T* grad_input_ptr, + T* grad_attn_qkvw_ptr, + T* grad_attn_qkvb_ptr, + T* grad_attn_ow_ptr, + T* grad_attn_ob_ptr, + T* grad_attn_nw_ptr, + T* grad_attn_nb_ptr, + T* grad_inter_w_ptr, + T* grad_inter_b_ptr, + T* grad_output_w_ptr, + T* grad_output_b_ptr, + T* grad_norm_w_ptr, + T* grad_norm_b_ptr) +{ + cublasSetStream(_cublasHandle, _stream); + + if (!_stochastic_mode) cudaStreamSynchronize(_stream); + + T* workspace = static_cast(Context::Instance().GetWorkSpace()); + size_t small_buf_size = bsz * _seq_length * _hidden_size; + T* buf_0 = workspace; + T* buf_1 = buf_0 + small_buf_size; + T* buf_2 = buf_1 + small_buf_size; + T* buf_3 = buf_2 + small_buf_size; + + T* ff2_buf = buf_3 + (_gelu_checkpoint ? 3 : 1) * small_buf_size; + T* ctx_bufB_ptr_recomp = ff2_buf + (_seq_length * _seq_length * bsz * _heads); + + cudaStream_t streams[2] = {_stream, _stream}; + + int bsz_seq = bsz * _seq_length; + int bsz_heads = bsz * _heads; + + if (!_pre_or_postLayerNorm) { + if (_norm_layer3.UseMean()) + _norm_layer3.Backward(bsz, + grad_output_ptr, + norm_w_ptr, + grad_norm_w_ptr, + grad_norm_b_ptr, + streams, + buf_1, + inp_norm_ptr); + + else + _norm_layer3.Backward(bsz, + grad_output_ptr, + norm_w_ptr, + norm_b_ptr, + grad_norm_w_ptr, + grad_norm_b_ptr, + streams, + buf_1, + output_ptr); + } + + if (_pre_or_postLayerNorm) + _layer_output_dropout.Backward(bsz_seq, buf_0, grad_output_ptr, _stream); + else + _layer_output_dropout.Backward(bsz_seq, buf_0, buf_1, _stream); + + const T* layer_dropout_buf = _layer_output_dropout.HasDropout() + ? buf_0 + : (_pre_or_postLayerNorm ? grad_output_ptr : buf_1); + + if (_gelu_checkpoint) _gelu.ForwardWithBiasAdd(bsz, ff2_inp_ptr, inter_b_ptr, buf_2, _stream); + _ff2.Backward(bsz_seq, + layer_dropout_buf, + (_gelu_checkpoint ? buf_2 : ff2_inp_ptr), + output_w_ptr, + grad_output_w_ptr, + grad_output_b_ptr, + _cublasHandle, + _stream, + ff2_buf); + + _gelu.Backward( + bsz, ff2_buf, (_gelu_checkpoint ? ff2_inp_ptr : gelu_inp_ptr), inter_b_ptr, _stream); + + _ff1.Backward(bsz_seq, + ff2_buf, + ff1_inp_ptr, + inter_w_ptr, + grad_inter_w_ptr, + grad_inter_b_ptr, + _cublasHandle, + _stream, + buf_3); + + if (!_pre_or_postLayerNorm) + launch_fused_add2(buf_2, buf_3, buf_1, bsz, _seq_length, _hidden_size, _stream); + + if (_pre_or_postLayerNorm) { + if (_norm_layer2.UseMean()) + _norm_layer2.BackwardFusedAdd(bsz, + buf_3, + grad_output_ptr, + attn_nw_ptr, + grad_attn_nw_ptr, + grad_attn_nb_ptr, + streams, + buf_0, + add_res_ptr); + + else + _norm_layer2.BackwardFusedAdd(bsz, + buf_3, + grad_output_ptr, + attn_nw_ptr, + attn_nb_ptr, + grad_attn_nw_ptr, + grad_attn_nb_ptr, + streams, + buf_0, + ff1_inp_ptr); + } else { + if (_norm_layer2.UseMean()) + _norm_layer2.Backward(bsz, + buf_2, + attn_nw_ptr, + grad_attn_nw_ptr, + grad_attn_nb_ptr, + streams, + buf_0, + add_res_ptr); + + else + _norm_layer2.Backward(bsz, + buf_2, + attn_nw_ptr, + attn_nb_ptr, + grad_attn_nw_ptr, + grad_attn_nb_ptr, + streams, + buf_0, + ff1_inp_ptr); + } + + _attn_output_dropout.Backward(bsz_seq, buf_2, buf_0, _stream); + + T* attn_output_dropout_buf = _attn_output_dropout.HasDropout() ? buf_2 : buf_0; + + _attn_out_linear.Backward(bsz_seq, + attn_output_dropout_buf, + attn_o_inp_ptr, + attn_ow_ptr, + grad_attn_ow_ptr, + grad_attn_ob_ptr, + _cublasHandle, + _stream, + buf_1); + + launch_transform_0213(buf_2, buf_1, bsz, _seq_length, _hidden_size, _heads, _stream); + + if (_attn_prob_dropout.HasDropout()) { + if (_attn_dropout_checkpoint) + _attn_prob_dropout.Forward( + bsz_heads * _seq_length, ctx_bufB_ptr_recomp, soft_out_ptr, _stream, true); + + _attn_context.Backward(bsz_heads, + buf_2, + v_tf_ptr, + (_attn_dropout_checkpoint ? ctx_bufB_ptr_recomp : ctx_bufB_ptr), + _cublasHandle, + buf_3, + ff2_buf); + } else + _attn_context.Backward( + bsz_heads, buf_2, v_tf_ptr, soft_out_ptr, _cublasHandle, buf_3, ff2_buf); + + _attn_prob_dropout.Backward(bsz_heads * _seq_length, ff2_buf, _stream); + + _softmax.Backward(bsz, ff2_buf, soft_out_ptr, _stream); + + _attn_scores.Backward(bsz_heads, ff2_buf, k_tf_ptr, q_tf_ptr, _cublasHandle, buf_2, buf_1); + + launch_transform4d_0213(ff2_buf, buf_1, bsz, _heads, _seq_length, _hidden_size, _stream, 3); + + if (_pre_or_postLayerNorm) + _qkv_linear.Backward(bsz_seq, + ff2_buf, + inp_norm_ptr, + attn_qkvw_ptr, + grad_attn_qkvw_ptr, + grad_attn_qkvb_ptr, + _cublasHandle, + _stream, + buf_2); + else + _qkv_linear.Backward(bsz_seq, + ff2_buf, + input_ptr, + attn_qkvw_ptr, + grad_attn_qkvw_ptr, + grad_attn_qkvb_ptr, + _cublasHandle, + _stream, + buf_2); + + if (_pre_or_postLayerNorm) { + if (_norm_layer3.UseMean()) + _norm_layer3.BackwardFusedAdd(bsz, + buf_2, + buf_0, + norm_w_ptr, + grad_norm_w_ptr, + grad_norm_b_ptr, + streams, + grad_input_ptr, + input_ptr); + + else + _norm_layer3.BackwardFusedAdd(bsz, + buf_2, + buf_0, + norm_w_ptr, + norm_b_ptr, + grad_norm_w_ptr, + grad_norm_b_ptr, + streams, + grad_input_ptr, + inp_norm_ptr); + } else + launch_fused_add2(grad_input_ptr, buf_2, buf_0, bsz, _seq_length, _hidden_size, _stream); +} + +template +void BertTransformerLayer::SetTrainingMode(bool training) +{ + // Dropout will be skipped when not in training model. + _attn_prob_dropout.SetTrainingMode(training); + _attn_output_dropout.SetTrainingMode(training); + _layer_output_dropout.SetTrainingMode(training); +} + +template +void BertTransformerLayer::SetIntermediateBuffers(uint8_t* attn_prob_dropout_mask_ptr, + uint8_t* attn_output_dropout_mask_ptr, + uint8_t* layer_output_dropout_mask_ptr) +{ + _attn_prob_dropout.SetMask(attn_prob_dropout_mask_ptr); + _attn_output_dropout.SetMask(attn_output_dropout_mask_ptr); + _layer_output_dropout.SetMask(layer_output_dropout_mask_ptr); +} + +template +int create_transformer_layer(int layer_id, + int batch_size, + int hidden_dim, + int num_heads, + int intermediate_size, + int seq_length, + float attn_dropout_ratio, + float hidden_dropout_ratio, + int seed, + bool pre_or_postLayerNorm, + bool test_gemm, + bool attn_dropout_checkpoint, + bool normalize_invertible, + bool gelu_checkpoint, + bool stochastic_mode) +{ + Context::Instance().SetSeed(seed); + Context::Instance().TestGemmFP16( + test_gemm, batch_size, seq_length, num_heads, hidden_dim / num_heads); + + auto layer = std::make_shared>(layer_id, + batch_size, + hidden_dim, + num_heads, + intermediate_size, + seq_length, + attn_dropout_ratio, + hidden_dropout_ratio, + pre_or_postLayerNorm, + Context::Instance().GetGemmAlgos(), + attn_dropout_checkpoint, + normalize_invertible, + gelu_checkpoint, + stochastic_mode); + + s_transformer_layers[layer_id] = layer; + + std::string dtype = (std::is_same::value) ? "half" : "float"; + + std::cout << "layer #" << layer_id << " is created with date type [" << dtype << "]." + << std::endl; + + return 0; +} + +template +std::vector ds_transformer_forward(int layer_id, + const torch::Tensor& input, + const torch::Tensor& input_mask, + const torch::Tensor& attn_qkvw, + const torch::Tensor& attn_qkvb, + const torch::Tensor& attn_ow, + const torch::Tensor& attn_ob, + const torch::Tensor& attn_nw, + const torch::Tensor& attn_nb, + const torch::Tensor& inter_w, + const torch::Tensor& inter_b, + const torch::Tensor& output_w, + const torch::Tensor& output_b, + const torch::Tensor& norm_w, + const torch::Tensor& norm_b, + bool training_mode, + bool prelayernorm, + bool attn_dropout_checkpoint, + bool normalize_invertible, + bool gelu_checkpoint) +{ + CHECK_INPUT(input); + CHECK_INPUT(input_mask); + CHECK_INPUT(attn_qkvw); + CHECK_INPUT(attn_qkvb); + CHECK_INPUT(attn_ow); + CHECK_INPUT(attn_ob); + CHECK_INPUT(attn_nw); + CHECK_INPUT(attn_nb); + CHECK_INPUT(inter_w); + CHECK_INPUT(inter_b); + CHECK_INPUT(output_w); + CHECK_INPUT(output_b); + CHECK_INPUT(norm_w); + CHECK_INPUT(norm_b); + + int bsz = input.size(0); + + const T* input_ptr = (const T*)input.data_ptr(); + const T* input_mask_ptr = (const T*)input_mask.data_ptr(); + const T* attn_qkvw_ptr = (const T*)attn_qkvw.data_ptr(); + const T* attn_qkvb_ptr = (const T*)attn_qkvb.data_ptr(); + const T* attn_ow_ptr = (const T*)attn_ow.data_ptr(); + const T* attn_ob_ptr = (const T*)attn_ob.data_ptr(); + const T* attn_nw_ptr = (const T*)attn_nw.data_ptr(); + const T* attn_nb_ptr = (const T*)attn_nb.data_ptr(); + const T* inter_w_ptr = (const T*)inter_w.data_ptr(); + const T* inter_b_ptr = (const T*)inter_b.data_ptr(); + const T* output_w_ptr = (const T*)output_w.data_ptr(); + const T* output_b_ptr = (const T*)output_b.data_ptr(); + const T* norm_w_ptr = (const T*)norm_w.data_ptr(); + const T* norm_b_ptr = (const T*)norm_b.data_ptr(); + + auto output = torch::empty_like(input); + T* out_ptr = (T*)output.data_ptr(); + + auto options = torch::TensorOptions() + .dtype(input.options().dtype()) + .layout(torch::kStrided) + .device(torch::kCUDA) + .requires_grad(true); + + auto uint8_options = torch::TensorOptions() + .dtype(torch::kInt8) + .layout(torch::kStrided) + .device(torch::kCUDA) + .requires_grad(false); + + std::shared_ptr> layer = + std::static_pointer_cast>(s_transformer_layers[layer_id]); + + auto inp_norm = ((prelayernorm || !normalize_invertible) ? torch::empty_like(input) : output); + auto add_res = (normalize_invertible ? inp_norm : torch::empty_like(input)); + auto attn_o_inp = torch::empty_like(input); + auto qkv_tf = torch::empty({(bsz * layer->GetSeqLength()), output_w.size(0) * 3}, options); + + auto attn_prob_dropout_mask = + torch::empty({(bsz * layer->GetNumHeads() * layer->GetSeqLength()), layer->GetSeqLength()}, + uint8_options); + auto attn_output_dropout_mask = + torch::empty({(bsz * layer->GetSeqLength()), layer->GetHiddenSize()}, uint8_options); + auto layer_output_dropout_mask = + torch::empty({(bsz * layer->GetSeqLength()), layer->GetHiddenSize()}, uint8_options); + + T* inp_norm_ptr = (T*)inp_norm.data_ptr(); + T* add_res_ptr = (T*)add_res.data_ptr(); + T* q_tf_ptr = (T*)qkv_tf.data_ptr(); + T* k_tf_ptr = + q_tf_ptr + (bsz * layer->GetSeqLength() * output_w.size(0)); //(T*)k_tf.data_ptr(); + T* v_tf_ptr = + k_tf_ptr + (bsz * layer->GetSeqLength() * output_w.size(0)); //(T*)v_tf.data_ptr(); + T* attn_o_inp_ptr = (T*)attn_o_inp.data_ptr(); + + torch::Tensor ff2_inp = + torch::empty({(bsz * layer->GetSeqLength()), output_w.size(1)}, options); + torch::Tensor gelu_inp = + (gelu_checkpoint + ? ff2_inp + : torch::empty({(bsz * layer->GetSeqLength()), output_w.size(1)}, options)); + auto ff1_inp = torch::empty_like(input); + T* ff2_inp_ptr = (T*)ff2_inp.data_ptr(); + T* gelu_inp_ptr = (T*)gelu_inp.data_ptr(); + T* ff1_inp_ptr = (T*)ff1_inp.data_ptr(); + + torch::Tensor soft_out = torch::empty( + {(bsz * layer->GetNumHeads() * layer->GetSeqLength()), layer->GetSeqLength()}, options); + torch::Tensor ctx_bufB = + (attn_dropout_checkpoint + ? soft_out + : torch::empty( + {(bsz * layer->GetNumHeads() * layer->GetSeqLength()), layer->GetSeqLength()}, + options)); + T* soft_out_ptr = (T*)soft_out.data_ptr(); + T* ctx_bufB_ptr = (T*)ctx_bufB.data_ptr(); + + layer->SetTrainingMode(training_mode); + layer->SetIntermediateBuffers((uint8_t*)attn_prob_dropout_mask.data_ptr(), + (uint8_t*)attn_output_dropout_mask.data_ptr(), + (uint8_t*)layer_output_dropout_mask.data_ptr()); + + layer->Forward(bsz, + input_ptr, + input_mask_ptr, + attn_qkvw_ptr, + attn_qkvb_ptr, + attn_ow_ptr, + attn_ob_ptr, + attn_nw_ptr, + attn_nb_ptr, + inter_w_ptr, + inter_b_ptr, + output_w_ptr, + output_b_ptr, + norm_w_ptr, + norm_b_ptr, + out_ptr, + inp_norm_ptr, + q_tf_ptr, + k_tf_ptr, + v_tf_ptr, + soft_out_ptr, + ctx_bufB_ptr, + attn_o_inp_ptr, + add_res_ptr, + ff1_inp_ptr, + gelu_inp_ptr, + ff2_inp_ptr); + + return {output, + inp_norm, + qkv_tf, + soft_out, + ctx_bufB, + attn_o_inp, + add_res, + ff1_inp, + gelu_inp, + ff2_inp, + attn_prob_dropout_mask, + attn_output_dropout_mask, + layer_output_dropout_mask}; +} + +template +std::vector ds_transformer_backward(int layer_id, + const torch::Tensor& grad_output, + const torch::Tensor& output, + const torch::Tensor& inp_norm, + const torch::Tensor& qkv_tf, + const torch::Tensor& soft_out, + const torch::Tensor& ctx_bufB, + const torch::Tensor& attn_o_inp, + const torch::Tensor& add_res, + const torch::Tensor& ff1_inp, + const torch::Tensor& gelu_inp, + const torch::Tensor& ff2_inp, + const torch::Tensor& attn_prob_dropout_mask, + const torch::Tensor& attn_output_dropout_mask, + const torch::Tensor& layer_output_dropout_mask, + const torch::Tensor& input, + const torch::Tensor& input_mask, + const torch::Tensor& attn_qkvw, + const torch::Tensor& attn_qkvb, + const torch::Tensor& attn_ow, + const torch::Tensor& attn_ob, + const torch::Tensor& attn_nw, + const torch::Tensor& attn_nb, + const torch::Tensor& inter_w, + const torch::Tensor& inter_b, + const torch::Tensor& output_w, + const torch::Tensor& output_b, + const torch::Tensor& norm_w, + const torch::Tensor& norm_b) +{ + auto g_output = grad_output.contiguous(); + CHECK_INPUT(g_output); + CHECK_INPUT(output); + CHECK_INPUT(inp_norm); + CHECK_INPUT(qkv_tf); + CHECK_INPUT(add_res); + CHECK_INPUT(soft_out); + CHECK_INPUT(ctx_bufB); + CHECK_INPUT(attn_o_inp); + CHECK_INPUT(ff1_inp); + CHECK_INPUT(gelu_inp); + CHECK_INPUT(ff2_inp); + CHECK_INPUT(input); + CHECK_INPUT(input_mask); + CHECK_INPUT(attn_qkvw); + CHECK_INPUT(attn_qkvb); + CHECK_INPUT(attn_ow); + CHECK_INPUT(attn_ob); + CHECK_INPUT(attn_nw); + CHECK_INPUT(attn_nb); + CHECK_INPUT(inter_w); + CHECK_INPUT(inter_b); + CHECK_INPUT(output_w); + CHECK_INPUT(output_b); + CHECK_INPUT(norm_w); + CHECK_INPUT(norm_b); + + int bsz = g_output.size(0); + std::shared_ptr> layer = + std::static_pointer_cast>(s_transformer_layers[layer_id]); + + auto grad_input = torch::empty_like(input); + auto grad_attn_qkvw = torch::empty_like(attn_qkvw); + auto grad_attn_qkvb = torch::empty_like(attn_qkvb); + auto grad_attn_ow = torch::empty_like(attn_ow); + auto grad_attn_ob = torch::empty_like(attn_ob); + auto grad_attn_nw = torch::empty_like(attn_nw); + auto grad_attn_nb = torch::empty_like(attn_nb); + auto grad_inter_w = torch::empty_like(inter_w); + auto grad_inter_b = torch::empty_like(inter_b); + auto grad_output_w = torch::empty_like(output_w); + auto grad_output_b = torch::empty_like(output_b); + auto grad_norm_w = torch::empty_like(norm_w); + auto grad_norm_b = torch::empty_like(norm_b); + + // inputs. + const T* grad_output_ptr = (const T*)g_output.data_ptr(); + const T* input_ptr = (const T*)input.data_ptr(); + const T* output_ptr = (const T*)output.data_ptr(); + const T* inp_norm_ptr = (const T*)inp_norm.data_ptr(); + const T* q_tf_ptr = (const T*)qkv_tf.data_ptr(); + const T* add_res_ptr = (const T*)add_res.data_ptr(); + const T* k_tf_ptr = + q_tf_ptr + (bsz * layer->GetSeqLength() * output_w.size(0)); //(const T*)k_tf.data_ptr(); + const T* v_tf_ptr = + k_tf_ptr + (bsz * layer->GetSeqLength() * output_w.size(0)); //(const T*)v_tf.data_ptr(); + const T* ff1_inp_ptr = (const T*)ff1_inp.data_ptr(); + const T* gelu_inp_ptr = (const T*)gelu_inp.data_ptr(); + const T* ff2_inp_ptr = (const T*)ff2_inp.data_ptr(); + const T* ctx_bufB_ptr = (const T*)ctx_bufB.data_ptr(); + const T* soft_out_ptr = (const T*)soft_out.data_ptr(); + const T* attn_o_inp_ptr = (const T*)attn_o_inp.data_ptr(); + const T* input_mask_ptr = (const T*)input_mask.data_ptr(); + const T* attn_qkvw_ptr = (const T*)attn_qkvw.data_ptr(); + const T* attn_ow_ptr = (const T*)attn_ow.data_ptr(); + const T* attn_nw_ptr = (const T*)attn_nw.data_ptr(); + const T* attn_nb_ptr = (const T*)attn_nb.data_ptr(); + const T* inter_w_ptr = (const T*)inter_w.data_ptr(); + const T* inter_b_ptr = (const T*)inter_b.data_ptr(); + const T* output_w_ptr = (const T*)output_w.data_ptr(); + const T* norm_w_ptr = (const T*)norm_w.data_ptr(); + const T* norm_b_ptr = (const T*)norm_b.data_ptr(); + + // outputs. + T* grad_input_ptr = (T*)grad_input.data_ptr(); + T* grad_attn_qkvw_ptr = (T*)grad_attn_qkvw.data_ptr(); + T* grad_attn_qkvb_ptr = (T*)grad_attn_qkvb.data_ptr(); + T* grad_attn_ow_ptr = (T*)grad_attn_ow.data_ptr(); + T* grad_attn_ob_ptr = (T*)grad_attn_ob.data_ptr(); + T* grad_attn_nw_ptr = (T*)grad_attn_nw.data_ptr(); + T* grad_attn_nb_ptr = (T*)grad_attn_nb.data_ptr(); + T* grad_inter_w_ptr = (T*)grad_inter_w.data_ptr(); + T* grad_inter_b_ptr = (T*)grad_inter_b.data_ptr(); + T* grad_output_w_ptr = (T*)grad_output_w.data_ptr(); + T* grad_output_b_ptr = (T*)grad_output_b.data_ptr(); + T* grad_norm_w_ptr = (T*)grad_norm_w.data_ptr(); + T* grad_norm_b_ptr = (T*)grad_norm_b.data_ptr(); + + layer->SetIntermediateBuffers((uint8_t*)attn_prob_dropout_mask.data_ptr(), + (uint8_t*)attn_output_dropout_mask.data_ptr(), + (uint8_t*)layer_output_dropout_mask.data_ptr()); + + layer->Backward(bsz, + grad_output_ptr, + input_ptr, + output_ptr, + inp_norm_ptr, + q_tf_ptr, + k_tf_ptr, + v_tf_ptr, + soft_out_ptr, + ctx_bufB_ptr, + attn_o_inp_ptr, + add_res_ptr, + ff1_inp_ptr, + gelu_inp_ptr, + ff2_inp_ptr, + input_mask_ptr, + attn_qkvw_ptr, + attn_ow_ptr, + attn_nw_ptr, + attn_nb_ptr, + inter_w_ptr, + inter_b_ptr, + output_w_ptr, + norm_w_ptr, + norm_b_ptr, + + grad_input_ptr, + grad_attn_qkvw_ptr, + grad_attn_qkvb_ptr, + grad_attn_ow_ptr, + grad_attn_ob_ptr, + grad_attn_nw_ptr, + grad_attn_nb_ptr, + grad_inter_w_ptr, + grad_inter_b_ptr, + grad_output_w_ptr, + grad_output_b_ptr, + grad_norm_w_ptr, + grad_norm_b_ptr); + + return {grad_input, + grad_attn_qkvw, + grad_attn_qkvb, + grad_attn_ow, + grad_attn_ob, + grad_attn_nw, + grad_attn_nb, + grad_inter_w, + grad_inter_b, + grad_output_w, + grad_output_b, + grad_norm_w, + grad_norm_b}; +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("forward_fp32", + &ds_transformer_forward, + "DeepSpeed Transformer forward with fp32 (CUDA)"); + m.def("forward_fp16", + &ds_transformer_forward<__half>, + "DeepSpeed Transformer forward with fp16 (CUDA)"); + m.def("backward_fp32", + &ds_transformer_backward, + "DeepSpeed Transformer backward with fp32 (CUDA)"); + m.def("backward_fp16", + &ds_transformer_backward<__half>, + "DeepSpeed Transformer backward with fp16 (CUDA)"); + m.def("create_transformer_layer_fp32", + &create_transformer_layer, + "Create DeepSpeed Transformer Transformer Layer with fp32 (CUDA)"); + m.def("create_transformer_layer_fp16", + &create_transformer_layer<__half>, + "Create DeepSpeed Transformer Transformer Layer with fp16 (CUDA)"); +} diff --git a/csrc/transformer/gelu_kernels.cu b/csrc/transformer/gelu_kernels.cu new file mode 100755 index 000000000000..f0e65e3829b5 --- /dev/null +++ b/csrc/transformer/gelu_kernels.cu @@ -0,0 +1,336 @@ +#include "custom_cuda_layers.h" + +inline __device__ float gelu(const float x) +{ + const float sqrt_param = 0.79788456080286535587989211986876f; + const float mul_param = 0.044715; + return x * 0.5f * (1.0f + tanhf(sqrt_param * (x + mul_param * x * x * x))); +} + +inline __device__ float d_gelu(const float x) +{ + const float sqrt_param = 0.79788456080286535587989211986876f; + const float mul_param = 0.044715; + + float x2mul = x * x * mul_param; + float tan_h = tanhf(sqrt_param * (x + x * x2mul)); + float dg1 = 0.5f * (1.0f + tan_h); + float dg2 = x * 0.5f * sqrt_param * (1 - tan_h * tan_h); + float dg3 = dg2 * 3 * x2mul; + return (dg1 + dg2 + dg3); +} + +/* +Fused bias add with GELU + +Loads a vector of 4 elements each iteration, for stride +iterations. It was written with the intention to launch 256 thread +threadblocks, so to launch for bert-large, we would set ITERATIONS +to 4. This is currently done automatically as a heuristic, setting +the number of iterations as blocks of 1024. + +For FP16, the values are loaded from memory as __half, but converted +to FP32 for the arithmetic itself, to prevent numerous overflow on +the intermediate hyperbolic tangent, since there's no intrinsic +that computes it directly. +*/ + +__global__ void gelu_kernel(const float* input, float* vals, int intermediate_size) +{ + int row = blockIdx.x; + int id = threadIdx.x; + int loop_stride = blockDim.x; + int iterations = intermediate_size / blockDim.x / 4; + int row_stride = intermediate_size / 4; + + const float4* input_cast = reinterpret_cast(input); + float4* vals_cast = reinterpret_cast(vals); + + for (int i = 0; i < iterations; i++) { + if (i * loop_stride + id < row_stride) { + float4 data = input_cast[row * row_stride + i * loop_stride + id]; + + data.x = gelu(data.x); + data.y = gelu(data.y); + data.z = gelu(data.z); + data.w = gelu(data.w); + + vals_cast[row * row_stride + i * loop_stride + id] = data; + } + } +} + +__global__ void gelu_kernel(const __half* input, __half* vals, int intermediate_size) +{ +#if __CUDA_ARCH__ >= 700 + int row = blockIdx.x; + int id = threadIdx.x; + int loop_stride = blockDim.x; + int iterations = intermediate_size / blockDim.x / 4; + int row_stride = intermediate_size / 4; + + const float2* input_cast = reinterpret_cast(input); + float2* vals_cast = reinterpret_cast(vals); + + for (int i = 0; i < iterations; i++) { + if (i * loop_stride + id < row_stride) { + float2 vals_vec = input_cast[row * row_stride + i * loop_stride + id]; + + __half2* vals_half = reinterpret_cast<__half2*>(&vals_vec); + + float2 low_data = __half22float2(vals_half[0]); + float2 high_data = __half22float2(vals_half[1]); + + low_data.x = gelu(low_data.x); + low_data.y = gelu(low_data.y); + high_data.x = gelu(high_data.x); + high_data.y = gelu(high_data.y); + + vals_half[0] = __float22half2_rn(low_data); + vals_half[1] = __float22half2_rn(high_data); + + vals_cast[row * row_stride + i * loop_stride + id] = vals_vec; + } + } +#endif +} + +__global__ void fused_bias_gelu(const float* input, + const float* bias, + float* vals, + int intermediate_size) +{ + int row = blockIdx.x; + int id = threadIdx.x; + int loop_stride = blockDim.x; + int iterations = intermediate_size / blockDim.x / 4; + int row_stride = intermediate_size / 4; + + const float4* input_cast = reinterpret_cast(input); + float4* vals_cast = reinterpret_cast(vals); + const float4* bias_cast = reinterpret_cast(bias); + + for (int i = 0; i < iterations; i++) { + if (i * loop_stride + id < row_stride) { + float4 data = input_cast[row * row_stride + i * loop_stride + id]; + float4 bias_data = bias_cast[i * loop_stride + id]; + + data.x += bias_data.x; + data.y += bias_data.y; + data.z += bias_data.z; + data.w += bias_data.w; + + data.x = gelu(data.x); + data.y = gelu(data.y); + data.z = gelu(data.z); + data.w = gelu(data.w); + + vals_cast[row * row_stride + i * loop_stride + id] = data; + } + } +} + +__global__ void fused_bias_gelu(const __half* input, + const __half* bias, + __half* vals, + int intermediate_size) +{ +#if __CUDA_ARCH__ >= 700 + int row = blockIdx.x; + int id = threadIdx.x; + int loop_stride = blockDim.x; + int iterations = intermediate_size / blockDim.x / 4; + int row_stride = intermediate_size / 4; + + const float2* input_cast = reinterpret_cast(input); + float2* vals_cast = reinterpret_cast(vals); + const float2* bias_cast = reinterpret_cast(bias); + + for (int i = 0; i < iterations; i++) { + if (i * loop_stride + id < row_stride) { + float2 vals_vec = input_cast[row * row_stride + i * loop_stride + id]; + float2 bias_vec = bias_cast[i * loop_stride + id]; + + __half2* vals_half = reinterpret_cast<__half2*>(&vals_vec); + __half2* bias_half = reinterpret_cast<__half2*>(&bias_vec); + + float2 low_data = __half22float2(vals_half[0]); + float2 high_data = __half22float2(vals_half[1]); + + float2 low_bias = __half22float2(bias_half[0]); + float2 high_bias = __half22float2(bias_half[1]); + + low_data.x += low_bias.x; + low_data.y += low_bias.y; + high_data.x += high_bias.x; + high_data.y += high_bias.y; + + low_data.x = gelu(low_data.x); + low_data.y = gelu(low_data.y); + high_data.x = gelu(high_data.x); + high_data.y = gelu(high_data.y); + + vals_half[0] = __float22half2_rn(low_data); + vals_half[1] = __float22half2_rn(high_data); + + vals_cast[row * row_stride + i * loop_stride + id] = vals_vec; + } + } +#endif +} + +__global__ void d_gelu_func(float* d_output, + const float* gelu_input, + const float* bias, + int intermediate_size) +{ + int row = blockIdx.x; + int id = threadIdx.x; + int loop_stride = blockDim.x; + int iterations = intermediate_size / blockDim.x / 4; + int row_stride = intermediate_size / 4; + + float4* d_output_cast = reinterpret_cast(d_output); + const float4* gelu_input_cast = reinterpret_cast(gelu_input); + const float4* bias_cast = reinterpret_cast(bias); + + for (int i = 0; i < iterations; i++) { + if (i * loop_stride + id < row_stride) { + float4 output_data = d_output_cast[row * row_stride + i * loop_stride + id]; + float4 gelu_input_data = gelu_input_cast[row * row_stride + i * loop_stride + id]; + float4 bias_data = bias_cast[i * loop_stride + id]; + + gelu_input_data.x += bias_data.x; + gelu_input_data.y += bias_data.y; + gelu_input_data.z += bias_data.z; + gelu_input_data.w += bias_data.w; + + output_data.x *= d_gelu(gelu_input_data.x); + output_data.y *= d_gelu(gelu_input_data.y); + output_data.z *= d_gelu(gelu_input_data.z); + output_data.w *= d_gelu(gelu_input_data.w); + + d_output_cast[row * row_stride + i * loop_stride + id] = output_data; + } + } +} + +__global__ void d_gelu_func(__half* d_output, + const __half* gelu_input, + const __half* bias, + int intermediate_size) +{ +#if __CUDA_ARCH__ >= 700 + int row = blockIdx.x; + int id = threadIdx.x; + int loop_stride = blockDim.x; + int iterations = intermediate_size / blockDim.x / 4; + int row_stride = intermediate_size / 4; + + float2* d_output_cast = reinterpret_cast(d_output); + const float2* gelu_input_cast = reinterpret_cast(gelu_input); + const float2* bias_cast = reinterpret_cast(bias); + +#pragma unroll + for (int i = 0; i < iterations; i++) { + if (i * loop_stride + id < row_stride) { + float2 output_data = d_output_cast[row * row_stride + i * loop_stride + id]; + float2 gelu_input_data = gelu_input_cast[row * row_stride + i * loop_stride + id]; + float2 bias_vec = bias_cast[i * loop_stride + id]; + + __half2* output_data_half = reinterpret_cast<__half2*>(&output_data); + __half2* gelu_input_data_half = reinterpret_cast<__half2*>(&gelu_input_data); + __half2* bias_half = reinterpret_cast<__half2*>(&bias_vec); + + float2 output_half_0 = __half22float2(output_data_half[0]); + float2 output_half_1 = __half22float2(output_data_half[1]); + + float2 gelu_input_half_0 = __half22float2(gelu_input_data_half[0]); + float2 gelu_input_half_1 = __half22float2(gelu_input_data_half[1]); + + float2 bias_half_0 = __half22float2(bias_half[0]); + float2 bias_half_1 = __half22float2(bias_half[1]); + + gelu_input_half_0.x += bias_half_0.x; + gelu_input_half_0.y += bias_half_0.y; + gelu_input_half_1.x += bias_half_1.x; + gelu_input_half_1.y += bias_half_1.y; + + output_half_0.x *= d_gelu(gelu_input_half_0.x); + output_half_0.y *= d_gelu(gelu_input_half_0.y); + output_half_1.x *= d_gelu(gelu_input_half_1.x); + output_half_1.y *= d_gelu(gelu_input_half_1.y); + + float2 result; + __half2* result_half2 = reinterpret_cast<__half2*>(&result); + + result_half2[0] = __float22half2_rn(output_half_0); + result_half2[1] = __float22half2_rn(output_half_1); + + d_output_cast[row * row_stride + i * loop_stride + id] = result; + } + } +#endif +} + +template +void launch_bias_gelu(const T* input, + const T* bias, + T* output, + int intermediate_size, + int batch_size, + int sequence_length, + cudaStream_t stream) +{ + int iterations = (intermediate_size + 1023) / 1024; + int threads = intermediate_size / iterations / 4; + dim3 block_dims(threads); + dim3 grid_dims(sequence_length * batch_size); + + fused_bias_gelu<<>>(input, bias, output, intermediate_size); +} + +template +void launch_gelu(const T* input, + T* output, + int intermediate_size, + int batch_size, + int sequence_length, + cudaStream_t stream) +{ + int iterations = (intermediate_size + 1023) / 1024; + int threads = intermediate_size / iterations / 4; + dim3 block_dims(threads); + dim3 grid_dims(sequence_length * batch_size); + + gelu_kernel<<>>(input, output, intermediate_size); +} + +template void +launch_bias_gelu(const float*, const float*, float*, int, int, int, cudaStream_t); +template void +launch_bias_gelu<__half>(const __half*, const __half*, __half*, int, int, int, cudaStream_t); + +template void launch_gelu(const float*, float*, int, int, int, cudaStream_t); +template void launch_gelu<__half>(const __half*, __half*, int, int, int, cudaStream_t); + +template +void launch_d_gelu(T* d_output, + const T* input, + const T* bias, + int intermediate_size, + int batch_size, + int sequence_length, + cudaStream_t stream) +{ + int iterations = (intermediate_size + 1023) / 1024; + int threads = intermediate_size / iterations / 4; + dim3 block_dims(threads); + dim3 grid_dims(sequence_length * batch_size); + + d_gelu_func<<>>(d_output, input, bias, intermediate_size); +} + +template void launch_d_gelu(float*, const float*, const float*, int, int, int, cudaStream_t); +template void +launch_d_gelu<__half>(__half*, const __half*, const __half*, int, int, int, cudaStream_t); diff --git a/csrc/transformer/general_kernels.cu b/csrc/transformer/general_kernels.cu new file mode 100644 index 000000000000..0ce280a702ab --- /dev/null +++ b/csrc/transformer/general_kernels.cu @@ -0,0 +1,416 @@ +#include "general_kernels.h" + +namespace cg = cooperative_groups; + +template +__global__ void column_sum_reduce(const T* __restrict__ inp, + T* __restrict__ out, + int rows, + int width) +{ + __shared__ float tile[TILE_DIM][TILE_DIM + 1]; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int idx = blockDim.x * blockIdx.x + threadIdx.x; + int offset = threadIdx.y * width + idx; + int y_stride = width * TILE_DIM; + + float localSum = 0; + + // Loop across matrix height + for (int r = threadIdx.y; r < rows; r += TILE_DIM) { + localSum += (float)inp[offset]; + offset += y_stride; + } + + tile[threadIdx.x][threadIdx.y] = localSum; + + __syncthreads(); + + // Sum the shared buffer. + float sum = tile[threadIdx.y][threadIdx.x]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < TILE_DIM; i <<= 1) sum += g.shfl_down(sum, i); + + if (threadIdx.x == 0) { + int pos = blockIdx.x * TILE_DIM + threadIdx.y; + out[pos] = sum; + } +} + +template +void launch_fuse_transpose_bias_kernel(const T* inp, + T* out, + int rows, + int cols, + cudaStream_t stream); + +template <> +void launch_fuse_transpose_bias_kernel(const float* inp, + float* out, + int rows, + int cols, + cudaStream_t stream) +{ + assert(rows % TILE_DIM == 0); + assert(cols % TILE_DIM == 0); + + dim3 grid_dim(cols / TILE_DIM); + dim3 block_dim(TILE_DIM, TILE_DIM); + + column_sum_reduce<<>>(inp, out, rows, cols); +} + +template <> +void launch_fuse_transpose_bias_kernel<__half>(const __half* inp, + __half* out, + int rows, + int cols, + cudaStream_t stream) +{ + assert(rows % TILE_DIM == 0); + assert(cols % TILE_DIM == 0); + + dim3 grid_dim(cols / TILE_DIM); + dim3 block_dim(TILE_DIM, TILE_DIM); + + column_sum_reduce<__half><<>>(inp, out, rows, cols); +} + +__global__ void fused_add2_kernel(float* out, + const float* inp1, + const float* inp2, + int size, + int row_stride) +{ + int row = blockIdx.x; + int id = threadIdx.x; + + const float4* inp1_4 = reinterpret_cast(inp1); + const float4* inp2_4 = reinterpret_cast(inp2); + float4* out_4 = reinterpret_cast(out); + + float4 val; + float4 inp1_reg = inp1_4[row * row_stride + id]; + float4 inp2_reg = inp2_4[row * row_stride + id]; + + val.x = inp1_reg.x + inp2_reg.x; + val.y = inp1_reg.y + inp2_reg.y; + val.z = inp1_reg.z + inp2_reg.z; + val.w = inp1_reg.w + inp2_reg.w; + + out_4[row * row_stride + id] = val; +} + +__global__ void fused_add2_kernel(__half* out, + const __half* inp1, + const __half* inp2, + int size, + int row_stride) +{ + int row = blockIdx.x; + int id = threadIdx.x; + + float2 inp1_4; + float2 inp2_4; + + __half2* inp1_h = reinterpret_cast<__half2*>(&inp1_4); + __half2* inp2_h = reinterpret_cast<__half2*>(&inp2_4); + + const float2* inp1_arr = reinterpret_cast(inp1); + const float2* inp2_arr = reinterpret_cast(inp2); + + inp1_4 = inp1_arr[row * row_stride + id]; + inp2_4 = inp2_arr[row * row_stride + id]; + + float2 inp1_h_f_0 = __half22float2(inp1_h[0]); + float2 inp1_h_f_1 = __half22float2(inp1_h[1]); + + float2 inp2_h_f_0 = __half22float2(inp2_h[0]); + float2 inp2_h_f_1 = __half22float2(inp2_h[1]); + + inp1_h_f_0.x += inp2_h_f_0.x; + inp1_h_f_0.y += inp2_h_f_0.y; + inp1_h_f_1.x += inp2_h_f_1.x; + inp1_h_f_1.y += inp2_h_f_1.y; + + float2 val_f; + __half2* val_h = reinterpret_cast<__half2*>(&val_f); + + val_h[0] = __float22half2_rn(inp1_h_f_0); + val_h[1] = __float22half2_rn(inp1_h_f_1); + + float2* out_4 = reinterpret_cast(out); + out_4[row * row_stride + id] = val_f; +} + +template <> +void launch_fused_add2(float* out, + const float* inp1, + const float* inp2, + int batch_size, + int seq_length, + int hidden_dim, + cudaStream_t& stream) +{ + dim3 grid_dim(batch_size * seq_length); + + dim3 block_dim(hidden_dim / 4); + + fused_add2_kernel<<>>( + out, inp1, inp2, (batch_size * seq_length * hidden_dim), hidden_dim / 4); +} + +template <> +void launch_fused_add2<__half>(__half* out, + const __half* inp1, + const __half* inp2, + int batch_size, + int seq_length, + int hidden_dim, + cudaStream_t& stream) +{ + dim3 grid_dim(batch_size * seq_length); + + dim3 block_dim(hidden_dim / 4); + + fused_add2_kernel<<>>( + out, inp1, inp2, (batch_size * seq_length * hidden_dim), hidden_dim / 4); +} + +__global__ void fused_add3_kernel(float* out, + const float* inp1, + const float* inp2, + const float* inp3, + int size, + int row_stride) +{ + int row = blockIdx.x; + int id = threadIdx.x; + + const float4* inp1_4 = reinterpret_cast(inp1); + const float4* inp2_4 = reinterpret_cast(inp2); + const float4* inp3_4 = reinterpret_cast(inp3); + + float4* out_4 = reinterpret_cast(out); + + float4 val; + float4 inp1_reg = inp1_4[row * row_stride + id]; + float4 inp2_reg = inp2_4[row * row_stride + id]; + float4 inp3_reg = inp3_4[row * row_stride + id]; + + val.x = inp1_reg.x + inp2_reg.x + inp3_reg.x; + val.y = inp1_reg.y + inp2_reg.y + inp3_reg.y; + val.z = inp1_reg.z + inp2_reg.z + inp3_reg.z; + val.w = inp1_reg.w + inp2_reg.w + inp3_reg.w; + + out_4[row * row_stride + id] = val; +} + +__global__ void fused_add3_kernel(__half* out, + const __half* inp1, + const __half* inp2, + const __half* inp3, + int size, + int row_stride) +{ + int row = blockIdx.x; + int id = threadIdx.x; + const float2* inp1_arr = reinterpret_cast(inp1); + const float2* inp2_arr = reinterpret_cast(inp2); + const float2* inp3_arr = reinterpret_cast(inp3); + + float2 inp1_4 = inp1_arr[row * row_stride + id]; + float2 inp2_4 = inp2_arr[row * row_stride + id]; + float2 inp3_4 = inp3_arr[row * row_stride + id]; + + __half2* inp1_h = reinterpret_cast<__half2*>(&inp1_4); + __half2* inp2_h = reinterpret_cast<__half2*>(&inp2_4); + __half2* inp3_h = reinterpret_cast<__half2*>(&inp3_4); + + float2 inp1_h_f_0 = __half22float2(inp1_h[0]); + float2 inp1_h_f_1 = __half22float2(inp1_h[1]); + + float2 inp2_h_f_0 = __half22float2(inp2_h[0]); + float2 inp2_h_f_1 = __half22float2(inp2_h[1]); + + float2 inp3_h_f_0 = __half22float2(inp3_h[0]); + float2 inp3_h_f_1 = __half22float2(inp3_h[1]); + + inp1_h_f_0.x += (inp2_h_f_0.x + inp3_h_f_0.x); + inp1_h_f_0.y += (inp2_h_f_0.y + inp3_h_f_0.y); + inp1_h_f_1.x += (inp2_h_f_1.x + inp3_h_f_1.x); + inp1_h_f_1.y += (inp2_h_f_1.y + inp3_h_f_1.y); + + float2 val_f; + __half2* val_h = reinterpret_cast<__half2*>(&val_f); + + val_h[0] = __float22half2_rn(inp1_h_f_0); + val_h[1] = __float22half2_rn(inp1_h_f_1); + + float2* out_4 = reinterpret_cast(out); + out_4[row * row_stride + id] = val_f; +} + +template <> +void launch_fused_add3(float* out, + const float* inp1, + const float* inp2, + const float* inp3, + int batch_size, + int seq_length, + int hidden_size, + cudaStream_t& stream) +{ + dim3 grid_dim(batch_size * seq_length); + + dim3 block_dim(hidden_size / 4); + + fused_add3_kernel<<>>( + out, inp1, inp2, inp3, (batch_size * seq_length * hidden_size), hidden_size / 4); +} + +template <> +void launch_fused_add3<__half>(__half* out, + const __half* inp1, + const __half* inp2, + const __half* inp3, + int batch_size, + int seq_length, + int hidden_size, + cudaStream_t& stream) +{ + dim3 grid_dim(batch_size * seq_length); + + dim3 block_dim(hidden_size / 4); + + fused_add3_kernel<<>>( + out, inp1, inp2, inp3, (batch_size * seq_length * hidden_size), hidden_size / 4); +} + +__global__ void fused_add4_kernel(float* out, + const float* inp1, + const float* inp2, + const float* inp3, + const float* inp4, + int size, + int row_stride) +{ + int row = blockIdx.x; + int id = threadIdx.x; + + const float4* inp1_4 = reinterpret_cast(inp1); + const float4* inp2_4 = reinterpret_cast(inp2); + const float4* inp3_4 = reinterpret_cast(inp3); + const float4* inp4_4 = reinterpret_cast(inp4); + float4* out_4 = reinterpret_cast(out); + + float4 val; + float4 inp1_reg = inp1_4[row * row_stride + id]; + float4 inp2_reg = inp2_4[row * row_stride + id]; + float4 inp3_reg = inp3_4[row * row_stride + id]; + float4 inp4_reg = inp4_4[row * row_stride + id]; + + val.x = inp1_reg.x + inp2_reg.x + inp3_reg.x + inp4_reg.x; + val.y = inp1_reg.y + inp2_reg.y + inp3_reg.y + inp4_reg.y; + val.z = inp1_reg.z + inp2_reg.z + inp3_reg.z + inp4_reg.z; + val.w = inp1_reg.w + inp2_reg.w + inp3_reg.w + inp4_reg.w; + + out_4[row * row_stride + id] = val; +} + +__global__ void fused_add4_kernel(__half* out, + const __half* inp1, + const __half* inp2, + const __half* inp3, + const __half* inp4, + int size, + int row_stride) +{ + int row = blockIdx.x; + int id = threadIdx.x; + const float2* inp1_arr = reinterpret_cast(inp1); + const float2* inp2_arr = reinterpret_cast(inp2); + const float2* inp3_arr = reinterpret_cast(inp3); + const float2* inp4_arr = reinterpret_cast(inp4); + + float2 inp1_4 = inp1_arr[row * row_stride + id]; + float2 inp2_4 = inp2_arr[row * row_stride + id]; + float2 inp3_4 = inp3_arr[row * row_stride + id]; + float2 inp4_4 = inp4_arr[row * row_stride + id]; + + __half2* inp1_h = reinterpret_cast<__half2*>(&inp1_4); + __half2* inp2_h = reinterpret_cast<__half2*>(&inp2_4); + __half2* inp3_h = reinterpret_cast<__half2*>(&inp3_4); + __half2* inp4_h = reinterpret_cast<__half2*>(&inp4_4); + + float2 inp1_h_f_0 = __half22float2(inp1_h[0]); + float2 inp1_h_f_1 = __half22float2(inp1_h[1]); + + float2 inp2_h_f_0 = __half22float2(inp2_h[0]); + float2 inp2_h_f_1 = __half22float2(inp2_h[1]); + + float2 inp3_h_f_0 = __half22float2(inp3_h[0]); + float2 inp3_h_f_1 = __half22float2(inp3_h[1]); + + float2 inp4_h_f_0 = __half22float2(inp4_h[0]); + float2 inp4_h_f_1 = __half22float2(inp4_h[1]); + + inp1_h_f_0.x += (inp2_h_f_0.x + inp3_h_f_0.x + inp4_h_f_0.x); + inp1_h_f_0.y += (inp2_h_f_0.y + inp3_h_f_0.y + inp4_h_f_0.y); + inp1_h_f_1.x += (inp2_h_f_1.x + inp3_h_f_1.x + inp4_h_f_1.x); + inp1_h_f_1.y += (inp2_h_f_1.y + inp3_h_f_1.y + inp4_h_f_1.y); + + float2 val_f; + __half2* val_h = reinterpret_cast<__half2*>(&val_f); + + val_h[0] = __float22half2_rn(inp1_h_f_0); + val_h[1] = __float22half2_rn(inp1_h_f_1); + + float2* out_4 = reinterpret_cast(out); + out_4[row * row_stride + id] = val_f; +} + +template <> +void launch_fused_add4(float* out, + const float* inp1, + const float* inp2, + const float* inp3, + const float* inp4, + int batch_size, + int seq_length, + int hidden_size, + cudaStream_t& stream) +{ + dim3 grid_dim(batch_size * seq_length); + + dim3 block_dim(hidden_size / 4); + + fused_add4_kernel<<>>( + out, inp1, inp2, inp3, inp4, (batch_size * seq_length * hidden_size), hidden_size / 4); +} + +template <> +void launch_fused_add4<__half>(__half* out, + const __half* inp1, + const __half* inp2, + const __half* inp3, + const __half* inp4, + int batch_size, + int seq_length, + int hidden_size, + cudaStream_t& stream) +{ + dim3 grid_dim(batch_size * seq_length); + + dim3 block_dim(hidden_size / 4); + + fused_add4_kernel<<>>( + out, inp1, inp2, inp3, inp4, (batch_size * seq_length * hidden_size), hidden_size / 4); +} diff --git a/csrc/transformer/normalize_kernels.cu b/csrc/transformer/normalize_kernels.cu new file mode 100755 index 000000000000..7345175694bf --- /dev/null +++ b/csrc/transformer/normalize_kernels.cu @@ -0,0 +1,2159 @@ +#include "custom_cuda_layers.h" + +namespace cg = cooperative_groups; + +/* +Fused bias add, residual (elementwise) add, and normalization layer. + +Unlike the GELU, which doesn't require template parameters, this layer does since it +does rely fairly heavily on unrolling loops. Currently, I exclude bounds checks and +assume that the number of elements is a multiple of a power of 2. Default behavior +for our purposes uses 256 threads for floats, and 128 threads for __half. This restriction +is a result of using the shift parameter to perform the minimum number of register file +shuffles necessary, which requires the number of threads in the secondary reduction to +be 1, 2, 4, 8, 16, or 32. The number of threads here corresponds to the number of complete +warps in the threadblock. + +For FP16, this kernel does not promote to FP32 in order to utilize the 2x throughput for +__half2 instructions, and avoid the conversion overhead (1/8 of __hal2 arithmetic). + +For specific launch constraints, see the launch functions. +*/ + +template +__global__ void fused_bias_residual_layer_norm(float* vals, + const float* residual, + const float* gamma, + const float* beta, + float epsilon, + bool preLayerNorm, + bool training = false, + float* vars = nullptr, + float* means = nullptr, + float* vals_hat = nullptr) +{ + constexpr int iteration_stride = row_stride / iterations; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile<32> g = cg::tiled_partition<32>(b); + + int row = blockIdx.x; + int id = threadIdx.x; + int gid = id / 32; + + float vals_arr[iterations]; + __shared__ float shr[iteration_stride >> 5]; + + float sum = 0.f; +#pragma unroll + for (int i = 0; i < iterations; i++) { + vals_arr[i] = residual[row * row_stride + i * iteration_stride + id]; + sum += vals_arr[i]; + } + + for (int i = 1; i < 32; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) shr[gid] = sum; + + b.sync(); + + if (g.thread_rank() < (iteration_stride >> 5)) sum = shr[g.thread_rank()]; + +#if !defined(__STOCHASTIC_MODE__) || __CUDA_ARCH__ < 700 + b.sync(); +#endif + + for (int i = 1; i < (iteration_stride >> 5); i *= 2) { sum += g.shfl_down(sum, i); } + + sum = g.shfl(sum, 0); + float mean = sum / row_stride; + if (training) + if (g.thread_rank() == 0) means[row] = mean; + float variance = 0.f; + for (int i = 0; i < iterations; i++) { + variance += (vals_arr[i] - mean) * (vals_arr[i] - mean); + } + + for (int i = 1; i < 32; i *= 2) { variance += g.shfl_down(variance, i); } + + if (g.thread_rank() == 0) shr[gid] = variance; + + b.sync(); + + if (g.thread_rank() < (iteration_stride >> 5)) variance = shr[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + b.sync(); +#endif + + for (int i = 1; i < (iteration_stride >> 5); i *= 2) { variance += g.shfl_down(variance, i); } + variance = g.shfl(variance, 0); + variance /= row_stride; + variance += epsilon; + if (training) + if (g.thread_rank() == 0) vars[row] = variance; + + for (int i = 0; i < iterations; i++) { + vals_arr[i] = (vals_arr[i] - mean) * rsqrtf(variance); + vals_arr[i] = + vals_arr[i] * gamma[i * iteration_stride + id] + beta[i * iteration_stride + id]; + vals[row * row_stride + i * iteration_stride + id] = vals_arr[i]; + } +} + +template +__global__ void fused_bias_residual_layer_norm(__half* vals, + const __half* residual, + const __half* gamma, + const __half* beta, + float epsilon, + bool preLayerNorm, + bool training = false, + __half* vars = nullptr, + __half* means = nullptr, + __half* vals_hat = nullptr) +{ +#if __CUDA_ARCH__ >= 700 + constexpr int iteration_stride = row_stride / iterations; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile<32> g = cg::tiled_partition<32>(b); + + int row = blockIdx.x; + int id = threadIdx.x; + int gid = id >> 5; + + __half2 vals_arr[iterations]; + float2 vals_f[iterations]; + __shared__ float shr[iteration_stride >> 5]; + + __half2* vals_cast = reinterpret_cast<__half2*>(vals); + const __half2* residual_cast = reinterpret_cast(residual); + + float sum = 0.f; +#pragma unroll + for (int i = 0; i < iterations; i++) { + vals_f[i] = __half22float2(residual_cast[row * row_stride + i * iteration_stride + id]); + sum += vals_f[i].x; + sum += vals_f[i].y; + } + + for (int i = 1; i < 32; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) shr[gid] = sum; + + b.sync(); + + if (g.thread_rank() < (iteration_stride >> 5)) sum = shr[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + b.sync(); +#endif + + for (int i = 1; i < (iteration_stride >> 5); i *= 2) { sum += g.shfl_down(sum, i); } + sum = g.shfl(sum, 0); + float mean = sum / (row_stride * 2); + + float variance = 0.f; + for (int i = 0; i < iterations; i++) { + variance += (vals_f[i].x - mean) * (vals_f[i].x - mean); + variance += (vals_f[i].y - mean) * (vals_f[i].y - mean); + } + + for (int i = 1; i < 32; i *= 2) { variance += g.shfl_down(variance, i); } + + if (g.thread_rank() == 0) shr[gid] = variance; + + b.sync(); + + if (g.thread_rank() < (iteration_stride >> 5)) variance = shr[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + b.sync(); +#endif + + for (int i = 1; i < (iteration_stride >> 5); i *= 2) { variance += g.shfl_down(variance, i); } + variance = g.shfl(variance, 0); + variance /= (row_stride * 2); + variance += epsilon; + + __half2 mean_h = __float2half2_rn(mean); + __half2 variance_h = __float2half2_rn(variance); + const __half2* gamma_cast = reinterpret_cast(gamma); + const __half2* beta_cast = reinterpret_cast(beta); + + if (training && g.thread_rank() == 0) { + vars[row] = __float2half(variance); + means[row] = __float2half(mean); + } + + for (int i = 0; i < iterations; i++) { + vals_arr[i] = __float22half2_rn(vals_f[i]); + vals_arr[i] = (vals_arr[i] - mean_h) * h2rsqrt(variance_h); + vals_arr[i] = vals_arr[i] * gamma_cast[i * iteration_stride + id] + + beta_cast[i * iteration_stride + id]; + vals_cast[row * row_stride + i * iteration_stride + id] = vals_arr[i]; + } +#endif +} + +template +void launch_bias_residual_layer_norm(T* vals, + const T* residual, + const T* gamma, + const T* beta, + float epsilon, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream, + bool preLayerNorm, + bool training, + T* vars, + T* means, + T* vals_hat); + +template <> +void launch_bias_residual_layer_norm(float* vals, + const float* residual, + const float* gamma, + const float* beta, + float epsilon, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream, + bool preLayerNorm, + bool training, + float* vars, + float* means, + float* vals_hat) +{ + constexpr int threads = THREADS; + + dim3 grid_dim(batch_size * sequence_length); + + dim3 block_dim(threads); + + // There are some limitations to call below functions, now just enumerate the situations. + if (hidden_dim == 768) + fused_bias_residual_layer_norm<768, 3><<>>( + vals, residual, gamma, beta, epsilon, preLayerNorm, training, vars, means, vals_hat); + else if (hidden_dim == 512) + fused_bias_residual_layer_norm<512, 2><<>>( + vals, residual, gamma, beta, epsilon, preLayerNorm, training, vars, means, vals_hat); + else if (hidden_dim == 1024) + fused_bias_residual_layer_norm<1024, 4><<>>( + vals, residual, gamma, beta, epsilon, preLayerNorm, training, vars, means, vals_hat); + else if (hidden_dim == 1536) + fused_bias_residual_layer_norm<1536, 6><<>>( + vals, residual, gamma, beta, epsilon, preLayerNorm, training, vars, means, vals_hat); + else if (hidden_dim == 2048) + fused_bias_residual_layer_norm<2048, 8><<>>( + vals, residual, gamma, beta, epsilon, preLayerNorm, training, vars, means, vals_hat); + else if (hidden_dim == 2560) + fused_bias_residual_layer_norm<2560, 10><<>>( + vals, residual, gamma, beta, epsilon, preLayerNorm, training, vars, means, vals_hat); + else + throw std::runtime_error("Unsupport hidden_dim."); +} + +template <> +void launch_bias_residual_layer_norm<__half>(__half* vals, + const __half* residual, + const __half* gamma, + const __half* beta, + float epsilon, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream, + bool preLayerNorm, + bool training, + __half* vars, + __half* means, + __half* vals_hat) +{ + constexpr int threads = 128; + + dim3 grid_dim(batch_size * sequence_length); + dim3 block_dim(threads); + + // There are some limitations to call below functions, now just enumerate the situations. + if (hidden_dim == 768) + fused_bias_residual_layer_norm<384, 3><<>>( + vals, residual, gamma, beta, epsilon, preLayerNorm, training, vars, means, vals_hat); + else if (hidden_dim == 512) + fused_bias_residual_layer_norm<256, 2><<>>( + vals, residual, gamma, beta, epsilon, preLayerNorm, training, vars, means, vals_hat); + else if (hidden_dim == 1024) + fused_bias_residual_layer_norm<512, 4><<>>( + vals, residual, gamma, beta, epsilon, preLayerNorm, training, vars, means, vals_hat); + else if (hidden_dim == 1536) + fused_bias_residual_layer_norm<768, 6><<>>( + vals, residual, gamma, beta, epsilon, preLayerNorm, training, vars, means, vals_hat); + else if (hidden_dim == 2048) + fused_bias_residual_layer_norm<1024, 8><<>>( + vals, residual, gamma, beta, epsilon, preLayerNorm, training, vars, means, vals_hat); + else if (hidden_dim == 2560) + fused_bias_residual_layer_norm<1280, 10><<>>( + vals, residual, gamma, beta, epsilon, preLayerNorm, training, vars, means, vals_hat); + else + throw std::runtime_error("Unsupport hidden_dim."); +} + +template +__global__ void fused_bias_residual_layer_norm(float* vals, + const float* residual, + const float* gamma, + const float* beta, + float epsilon, + bool preLayerNorm, + bool training = false, + float* vars = nullptr, + float* vals_hat = nullptr, + bool save_vals = false) +{ + constexpr int iteration_stride = row_stride / iterations; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile<32> g = cg::tiled_partition<32>(b); + + int row = blockIdx.x; + int id = threadIdx.x; + int gid = id / 32; + + float vals_arr[iterations]; + __shared__ float shr[iteration_stride >> 5]; + + float sum = 0.f; +#pragma unroll + for (int i = 0; i < iterations; i++) { + vals_arr[i] = residual[row * row_stride + i * iteration_stride + id]; + sum += vals_arr[i]; + } + + for (int i = 1; i < 32; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) shr[gid] = sum; + + b.sync(); + + if (g.thread_rank() < (iteration_stride >> 5)) sum = shr[g.thread_rank()]; + +#if !defined(__STOCHASTIC_MODE__) || __CUDA_ARCH__ < 700 + b.sync(); +#endif + + for (int i = 1; i < (iteration_stride >> 5); i *= 2) { sum += g.shfl_down(sum, i); } + + sum = g.shfl(sum, 0); + float mean = sum / row_stride; + float variance = 0.f; + for (int i = 0; i < iterations; i++) { + variance += (vals_arr[i] - mean) * (vals_arr[i] - mean); + } + + for (int i = 1; i < 32; i *= 2) { variance += g.shfl_down(variance, i); } + + if (g.thread_rank() == 0) shr[gid] = variance; + + b.sync(); + + if (g.thread_rank() < (iteration_stride >> 5)) variance = shr[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + b.sync(); +#endif + + for (int i = 1; i < (iteration_stride >> 5); i *= 2) { variance += g.shfl_down(variance, i); } + variance = g.shfl(variance, 0); + variance /= row_stride; + variance += epsilon; + if (training) + if (g.thread_rank() == 0) vars[row] = variance; + + for (int i = 0; i < iterations; i++) { + vals_arr[i] = (vals_arr[i] - mean) * rsqrtf(variance); + vals_arr[i] = + vals_arr[i] * gamma[i * iteration_stride + id] + beta[i * iteration_stride + id]; + vals[row * row_stride + i * iteration_stride + id] = vals_arr[i]; + } +} + +template +__global__ void fused_bias_residual_layer_norm(__half* vals, + const __half* residual, + const __half* gamma, + const __half* beta, + float epsilon, + bool preLayerNorm, + bool training = false, + __half* vars = nullptr, + __half* vals_hat = nullptr, + bool save_vals = false) +{ +#if __CUDA_ARCH__ >= 700 + constexpr int iteration_stride = row_stride / iterations; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile<32> g = cg::tiled_partition<32>(b); + + int row = blockIdx.x; + int id = threadIdx.x; + int gid = id >> 5; + + __half2 vals_arr[iterations]; + float2 vals_f[iterations]; + __shared__ float shr[iteration_stride >> 5]; + + __half2* vals_cast = reinterpret_cast<__half2*>(vals); + const __half2* residual_cast = reinterpret_cast(residual); + + float sum = 0.f; +#pragma unroll + for (int i = 0; i < iterations; i++) { + vals_f[i] = __half22float2(residual_cast[row * row_stride + i * iteration_stride + id]); + sum += vals_f[i].x; + sum += vals_f[i].y; + } + + for (int i = 1; i < 32; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) shr[gid] = sum; + + b.sync(); + + if (g.thread_rank() < (iteration_stride >> 5)) sum = shr[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + b.sync(); +#endif + + for (int i = 1; i < (iteration_stride >> 5); i *= 2) { sum += g.shfl_down(sum, i); } + sum = g.shfl(sum, 0); + float mean = sum / (row_stride * 2); + + float variance = 0.f; + for (int i = 0; i < iterations; i++) { + variance += (vals_f[i].x - mean) * (vals_f[i].x - mean); + variance += (vals_f[i].y - mean) * (vals_f[i].y - mean); + } + + for (int i = 1; i < 32; i *= 2) { variance += g.shfl_down(variance, i); } + + if (g.thread_rank() == 0) shr[gid] = variance; + + b.sync(); + + if (g.thread_rank() < (iteration_stride >> 5)) variance = shr[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + b.sync(); +#endif + + for (int i = 1; i < (iteration_stride >> 5); i *= 2) { variance += g.shfl_down(variance, i); } + variance = g.shfl(variance, 0); + variance /= (row_stride * 2); + variance += epsilon; + + __half2 mean_h = __float2half2_rn(mean); + __half2 variance_h = __float2half2_rn(variance); + const __half2* gamma_cast = reinterpret_cast(gamma); + const __half2* beta_cast = reinterpret_cast(beta); + + if (training && g.thread_rank() == 0) vars[row] = __float2half(variance); + + for (int i = 0; i < iterations; i++) { + vals_arr[i] = __float22half2_rn(vals_f[i]); + vals_arr[i] = (vals_arr[i] - mean_h) * h2rsqrt(variance_h); + vals_arr[i] = vals_arr[i] * gamma_cast[i * iteration_stride + id] + + beta_cast[i * iteration_stride + id]; + vals_cast[row * row_stride + i * iteration_stride + id] = vals_arr[i]; + } +#endif +} + +template +void launch_bias_residual_layer_norm(T* vals, + const T* residual, + const T* gamma, + const T* beta, + float epsilon, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream, + bool preLayerNorm, + bool training, + T* vars, + T* vals_hat, + bool save_vals); + +/* +To tune this launch the following restrictions must be met: + +For float: +row_stride == hidden_size +threads * iterations == row_stride +threads is in [32, 64, 128, 256, 512, 1024] + +For half: +row_stride == hidden_size / 2 +threads * iterations == row_stride +threads is in [32, 64, 128, 256, 512, 1024] + +*/ + +template <> +void launch_bias_residual_layer_norm(float* vals, + const float* residual, + const float* gamma, + const float* beta, + float epsilon, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream, + bool preLayerNorm, + bool training, + float* vars, + float* vals_hat, + bool save_vals) +{ + constexpr int threads = THREADS; + + dim3 grid_dim(batch_size * sequence_length); + + dim3 block_dim(threads); + + // There are some limitations to call below functions, now just enumerate the situations. + if (hidden_dim == 768) + fused_bias_residual_layer_norm<768, 3><<>>(vals, + residual, + gamma, + beta, + epsilon, + preLayerNorm, + training, + vars, + vals_hat, + save_vals); + else if (hidden_dim == 512) + fused_bias_residual_layer_norm<512, 2><<>>(vals, + residual, + gamma, + beta, + epsilon, + preLayerNorm, + training, + vars, + vals_hat, + save_vals); + else if (hidden_dim == 1024) + fused_bias_residual_layer_norm<1024, 4><<>>(vals, + residual, + gamma, + beta, + epsilon, + preLayerNorm, + training, + vars, + vals_hat, + save_vals); + else if (hidden_dim == 1536) + fused_bias_residual_layer_norm<1536, 6><<>>(vals, + residual, + gamma, + beta, + epsilon, + preLayerNorm, + training, + vars, + vals_hat, + save_vals); + else if (hidden_dim == 2048) + fused_bias_residual_layer_norm<2048, 8><<>>(vals, + residual, + gamma, + beta, + epsilon, + preLayerNorm, + training, + vars, + vals_hat, + save_vals); + else if (hidden_dim == 2560) + fused_bias_residual_layer_norm<2560, 10><<>>(vals, + residual, + gamma, + beta, + epsilon, + preLayerNorm, + training, + vars, + vals_hat, + save_vals); + else + throw std::runtime_error("Unsupport hidden_dim."); +} + +template <> +void launch_bias_residual_layer_norm<__half>(__half* vals, + const __half* residual, + const __half* gamma, + const __half* beta, + float epsilon, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream, + bool preLayerNorm, + bool training, + __half* vars, + __half* vals_hat, + bool save_vals) +{ + constexpr int threads = 128; + + dim3 grid_dim(batch_size * sequence_length); + dim3 block_dim(threads); + + // There are some limitations to call below functions, now just enumerate the situations. + if (hidden_dim == 768) + fused_bias_residual_layer_norm<384, 3><<>>(vals, + residual, + gamma, + beta, + epsilon, + preLayerNorm, + training, + vars, + vals_hat, + save_vals); + else if (hidden_dim == 512) + fused_bias_residual_layer_norm<256, 2><<>>(vals, + residual, + gamma, + beta, + epsilon, + preLayerNorm, + training, + vars, + vals_hat, + save_vals); + else if (hidden_dim == 1024) + fused_bias_residual_layer_norm<512, 4><<>>(vals, + residual, + gamma, + beta, + epsilon, + preLayerNorm, + training, + vars, + vals_hat, + save_vals); + else if (hidden_dim == 1536) + fused_bias_residual_layer_norm<768, 6><<>>(vals, + residual, + gamma, + beta, + epsilon, + preLayerNorm, + training, + vars, + vals_hat, + save_vals); + else if (hidden_dim == 2048) + fused_bias_residual_layer_norm<1024, 8><<>>(vals, + residual, + gamma, + beta, + epsilon, + preLayerNorm, + training, + vars, + vals_hat, + save_vals); + else if (hidden_dim == 2560) + fused_bias_residual_layer_norm<1280, 10><<>>(vals, + residual, + gamma, + beta, + epsilon, + preLayerNorm, + training, + vars, + vals_hat, + save_vals); + else + throw std::runtime_error("Unsupport hidden_dim."); +} + +/* Normalize Gamma & Betta gradients + * Compute gradients using either X_hat or + * normalize input (invertible). + * Combine transpose with gradients computation. + */ + +template +__global__ void LayerNormBackward1(const T* __restrict__ out_grad, + const T* __restrict__ vals_hat, + const T* __restrict__ gamma, + const T* __restrict__ betta, + T* __restrict__ gamma_grad, + T* __restrict__ betta_grad, + int rows, + int width, + bool invertible) +{ + __shared__ float betta_buffer[TILE_DIM][TILE_DIM + 1]; + __shared__ float gamma_buffer[TILE_DIM][TILE_DIM + 1]; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int idx = blockDim.x * blockIdx.x + threadIdx.x; + int offset = threadIdx.y * width + idx; + int y_stride = width * TILE_DIM; + + int pos = blockIdx.x * TILE_DIM + threadIdx.y; + float betta_reg = (invertible ? (float)betta[pos] : 0.0f); + float gamma_reg = (float)gamma[pos]; + + // Loop across matrix height + float betta_tmp = 0; + float gamma_tmp = 0; + for (int r = threadIdx.y; r < rows; r += TILE_DIM) { + float grad = (float)out_grad[offset]; + float val = (invertible ? ((float)vals_hat[offset] - betta_reg) / gamma_reg + : (float)vals_hat[offset]); + betta_tmp += grad; + gamma_tmp += (val * grad); + + offset += y_stride; + } + + betta_buffer[threadIdx.x][threadIdx.y] = betta_tmp; + gamma_buffer[threadIdx.x][threadIdx.y] = gamma_tmp; + + __syncthreads(); + + // Sum the shared buffer. + float s1 = betta_buffer[threadIdx.y][threadIdx.x]; + float s2 = gamma_buffer[threadIdx.y][threadIdx.x]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < TILE_DIM; i <<= 1) { + s1 += g.shfl_down(s1, i); + s2 += g.shfl_down(s2, i); + } + + if (threadIdx.x == 0) { + betta_grad[pos] = s1; + gamma_grad[pos] = s2; + } +} + +/* Normalize Gamma & Betta gradients + * Compute gradients using the input to + * the normalize. + * Combine transpose with gradients computation. + */ + +template +__global__ void LayerNormBackward1(const T* __restrict__ out_grad, + const T* __restrict__ X_data, + const T* __restrict__ vars, + const T* __restrict__ means, + T* __restrict__ gamma_grad, + T* __restrict__ betta_grad, + int rows, + int width) +{ + __shared__ float betta_buffer[TILE_DIM][TILE_DIM + 1]; + __shared__ float gamma_buffer[TILE_DIM][TILE_DIM + 1]; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int idx = blockDim.x * blockIdx.x + threadIdx.x; + int offset = threadIdx.y * width + idx; + int y_stride = width * TILE_DIM; + + int pos = blockIdx.x * TILE_DIM + threadIdx.y; + // Loop across matrix height + + float betta_tmp = 0; + float gamma_tmp = 0; + for (int r = threadIdx.y; r < rows; r += TILE_DIM) { + float grad = (float)out_grad[offset]; + float val = (float)X_data[offset]; + val = (val - (float)means[r]) * rsqrtf((float)vars[r]); + betta_tmp += grad; + gamma_tmp += (val * grad); + + offset += y_stride; + } + + betta_buffer[threadIdx.x][threadIdx.y] = betta_tmp; + gamma_buffer[threadIdx.x][threadIdx.y] = gamma_tmp; + + __syncthreads(); + + // Sum the shared buffer. + float s1 = betta_buffer[threadIdx.y][threadIdx.x]; + float s2 = gamma_buffer[threadIdx.y][threadIdx.x]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < TILE_DIM; i <<= 1) { + s1 += g.shfl_down(s1, i); + s2 += g.shfl_down(s2, i); + } + + if (threadIdx.x == 0) { + betta_grad[pos] = s1; + gamma_grad[pos] = s2; + } +} +/* + +/* Backward Normalize (Input-Gradient) + * Using the means and variances from the input + * This type of backward is invertible! + * We do the backward using the X_hat (X - u) / sqrt(variance) or the output of Normalization. + */ + +template // Hidden_Dim +__global__ void LayerNormBackward2(const float* out_grad, + const float* vals_hat, + const float* gamma, + const float* betta, + const float* vars, + float* inp_grad, + bool invertible) +{ + constexpr int iterations = row_stride / THREADS; + constexpr int iteration_stride = THREADS; // row_stride / iterations; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int row = blockIdx.x; + int id = threadIdx.x; + int wid = id / WARP_SIZE; + constexpr int warp_num = (THREADS < row_stride ? THREADS : row_stride) / WARP_SIZE; + __shared__ float partialSum[warp_num]; + + float vals_arr[iterations]; + float vals_hat_arr[iterations]; + +#pragma unroll + for (int i = 0; i < iterations; i++) { + float gamma_reg = gamma[i * iteration_stride + id]; + vals_arr[i] = out_grad[row * row_stride + i * iteration_stride + id]; + vals_arr[i] *= gamma_reg; + vals_hat_arr[i] = (invertible ? (vals_hat[row * row_stride + i * iteration_stride + id] - + betta[i * iteration_stride + id]) / + gamma_reg + : vals_hat[row * row_stride + i * iteration_stride + id]); + } + + float var_reg = vars[row]; + + float sum = 0; + for (int i = 0; i < iterations; i++) { + sum += vals_hat_arr[i] * vals_arr[i] * + sqrtf(var_reg); // dval_hat = gamma * (x - u) * out_grad + vals_arr[i] *= rsqrtf(var_reg); // dvar_inv = gamma * out_grad / sqrt(var) + } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + + sum = g.shfl(sum, 0); + sum /= row_stride; + + for (int i = 0; i < iterations; i++) { vals_arr[i] += ((-sum * vals_hat_arr[i]) / var_reg); } + + sum = 0; + for (int i = 0; i < iterations; i++) { sum += vals_arr[i]; } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + sum = g.shfl(sum, 0); + sum /= row_stride; + + for (int i = 0; i < iterations; i++) + inp_grad[row * row_stride + i * iteration_stride + id] = (vals_arr[i] - sum); +} + +template // Hidden_Dim +__global__ void LayerNormBackward2(const __half* out_grad, + const __half* vals_hat, + const __half* gamma, + const __half* betta, + const __half* vars, + __half* inp_grad, + bool invertible) +{ + constexpr int iteration_stride = THREADS / 2; // row_stride / iterations; + constexpr int iterations = row_stride / iteration_stride; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int row = blockIdx.x; + int id = threadIdx.x; + int wid = id / WARP_SIZE; + constexpr int warp_num = + (iteration_stride < row_stride ? iteration_stride : row_stride) / WARP_SIZE; + __shared__ float partialSum[warp_num]; + + __half2 vals_arr[iterations]; + float2 vals_arr_f[iterations]; + __half2 vals_hat_arr[iterations]; + + __half2* inp_grad_h = reinterpret_cast<__half2*>(inp_grad); + const __half2* out_grad_h = reinterpret_cast(out_grad); + const __half2* vals_hat_h = reinterpret_cast(vals_hat); + + const __half2* gamma_h = reinterpret_cast(gamma); + const __half2* betta_h = (invertible ? reinterpret_cast(betta) : nullptr); + +#pragma unroll + for (int i = 0; i < iterations; i++) { + __half2 gamma_reg = gamma_h[i * iteration_stride + id]; + vals_arr[i] = out_grad_h[row * row_stride + i * iteration_stride + id]; + vals_arr[i] *= gamma_reg; + vals_hat_arr[i] = (invertible ? (vals_hat_h[row * row_stride + i * iteration_stride + id] - + betta_h[i * iteration_stride + id]) / + gamma_reg + : vals_hat_h[row * row_stride + i * iteration_stride + id]); + } + __half var_h = vars[row]; + __half2 var_reg = __halves2half2(var_h, var_h); + + float sum = 0.f; + for (int i = 0; i < iterations; i++) { + __half2 result_h = (vals_hat_arr[i] * vals_arr[i] * h2sqrt(var_reg)); + float2 result_f = __half22float2(result_h); + sum += result_f.x; + sum += result_f.y; + vals_arr[i] *= h2rsqrt(var_reg); + } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + + sum = g.shfl(sum, 0); + sum /= (2 * row_stride); + __half2 sum_h = __float2half2_rn(sum); + + for (int i = 0; i < iterations; i++) { + __half2 temp = ((-sum_h * vals_hat_arr[i]) / (var_reg)); + vals_arr_f[i] = __half22float2(vals_arr[i]); + float2 temp_f = __half22float2(temp); + vals_arr_f[i].x += temp_f.x; + vals_arr_f[i].y += temp_f.y; + } + sum = 0.f; + + for (int i = 0; i < iterations; i++) { + sum += (vals_arr_f[i].x); + sum += (vals_arr_f[i].y); + } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + + sum = g.shfl(sum, 0); + sum /= (2 * row_stride); + + for (int i = 0; i < iterations; i++) { + vals_arr_f[i].x -= sum; + vals_arr_f[i].y -= sum; + __half2 temp = __float22half2_rn(vals_arr_f[i]); + + inp_grad_h[row * row_stride + i * iteration_stride + id] = temp; + } +} + +template <> +void launch_layerNorm_backward(const float* out_grad, + const float* vals_hat, + const float* vars, + const float* gamma, + float* gamma_grad, + float* betta_grad, + float* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2], + bool invertible, + const float* betta) +{ + constexpr int threads = THREADS; + int batch = batch_size * sequence_length; + + dim3 grid_dim(hidden_dim / TILE_DIM); + dim3 block_dim(TILE_DIM, TILE_DIM); + + LayerNormBackward1<<>>( + out_grad, vals_hat, gamma, betta, gamma_grad, betta_grad, batch, hidden_dim, invertible); + + dim3 grid_dim2(batch); + dim3 block_dim2(threads); + + if (hidden_dim == 768) + LayerNormBackward2<768><<>>( + out_grad, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 512) + LayerNormBackward2<512><<>>( + out_grad, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 1024) + LayerNormBackward2<1024><<>>( + out_grad, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 1536) + LayerNormBackward2<1536><<>>( + out_grad, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 2048) + LayerNormBackward2<2048><<>>( + out_grad, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 2560) + LayerNormBackward2<2560><<>>( + out_grad, vals_hat, gamma, betta, vars, inp_grad, invertible); + else + throw std::runtime_error("Unsupport hidden_dim."); +} + +template <> +void launch_layerNorm_backward<__half>(const __half* out_grad, + const __half* vals_hat, + const __half* vars, + const __half* gamma, + __half* gamma_grad, + __half* betta_grad, + __half* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2], + bool invertible, + const __half* betta) +{ + constexpr int threads = THREADS; + int batch = batch_size * sequence_length; + + dim3 grid_dim(hidden_dim / TILE_DIM); + dim3 block_dim(TILE_DIM, TILE_DIM); + + LayerNormBackward1<__half><<>>( + out_grad, vals_hat, gamma, betta, gamma_grad, betta_grad, batch, hidden_dim, invertible); + + dim3 grid_dim2(batch); + dim3 block_dim2(threads / 2); + + if (hidden_dim == 768) + LayerNormBackward2<384><<>>( + out_grad, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 512) + LayerNormBackward2<256><<>>( + out_grad, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 1024) + LayerNormBackward2<512><<>>( + out_grad, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 1536) + LayerNormBackward2<768><<>>( + out_grad, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 2048) + LayerNormBackward2<1024><<>>( + out_grad, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 2560) + LayerNormBackward2<1280><<>>( + out_grad, vals_hat, gamma, betta, vars, inp_grad, invertible); + else + throw std::runtime_error("Unsupport hidden_dim."); +} + +/* Backward Normalize (Input-Gradient) + * Using the means and variances from the input + * This type of backward is not invertible! + * We do the backward using the input (X) + */ + +template // Hidden_Dim +__global__ void LayerNormBackward2(const float* out_grad, + const float* X_vals, + const float* gamma, + const float* vars, + const float* means, + float* inp_grad) +{ + constexpr int iterations = row_stride / THREADS; + constexpr int iteration_stride = THREADS; // row_stride / iterations; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int row = blockIdx.x; + int id = threadIdx.x; + int wid = id / WARP_SIZE; + constexpr int warp_num = (THREADS < row_stride ? THREADS : row_stride) / WARP_SIZE; + __shared__ float partialSum[warp_num]; + + float vals_arr[iterations]; + +#pragma unroll + for (int i = 0; i < iterations; i++) { + float gamma_reg = gamma[i * iteration_stride + id]; + vals_arr[i] = out_grad[row * row_stride + i * iteration_stride + id]; + vals_arr[i] *= gamma_reg; + } + + float var_reg = vars[row]; + float mean_reg = means[row]; + + float sum = 0; + float xu[iterations]; + for (int i = 0; i < iterations; i++) { + xu[i] = (X_vals[row * row_stride + i * iteration_stride + id] - mean_reg); + sum += vals_arr[i] * xu[i]; + vals_arr[i] *= rsqrtf(var_reg); + } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + + sum = g.shfl(sum, 0); + sum /= row_stride; + + for (int i = 0; i < iterations; i++) { + vals_arr[i] += (-sum * xu[i] * rsqrtf(var_reg) / (var_reg)); + } + + sum = 0; + for (int i = 0; i < iterations; i++) { sum += vals_arr[i]; } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + sum = g.shfl(sum, 0); + sum /= row_stride; + + for (int i = 0; i < iterations; i++) + inp_grad[row * row_stride + i * iteration_stride + id] = (vals_arr[i] - sum); +} + +template // Hidden_Dim +__global__ void LayerNormBackward2(const __half* out_grad, + const __half* X_vals, + const __half* gamma, + const __half* vars, + const __half* means, + __half* inp_grad) +{ + constexpr int iteration_stride = THREADS / 2; // row_stride / iterations; + constexpr int iterations = row_stride / iteration_stride; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int row = blockIdx.x; + int id = threadIdx.x; + int wid = id / WARP_SIZE; + constexpr int warp_num = + (iteration_stride < row_stride ? iteration_stride : row_stride) / WARP_SIZE; + + __shared__ float partialSum[warp_num]; + + __half2 vals_arr[iterations]; + float2 vals_arr_f[iterations]; + + __half2* inp_grad_h = reinterpret_cast<__half2*>(inp_grad); + const __half2* out_grad_h = reinterpret_cast(out_grad); + const __half2* vals_hat_h = reinterpret_cast(X_vals); + + const __half2* gamma_h = reinterpret_cast(gamma); + +#pragma unroll + for (int i = 0; i < iterations; i++) { + __half2 gamma_reg = gamma_h[i * iteration_stride + id]; + vals_arr[i] = out_grad_h[row * row_stride + i * iteration_stride + id]; + vals_arr[i] *= gamma_reg; // out_grad * gamma + } + __half mean_h = means[row]; + __half var_h = vars[row]; + __half2 var_reg = __halves2half2(var_h, var_h); + __half2 mean_reg = __halves2half2(mean_h, mean_h); + __half2 xu[iterations]; + + float sum = 0.f; + for (int i = 0; i < iterations; i++) { + xu[i] = (vals_hat_h[row * row_stride + i * iteration_stride + id] - mean_reg); + __half2 result_h = (xu[i] * vals_arr[i]); + float2 result_f = __half22float2(result_h); + sum += result_f.x; + sum += result_f.y; + vals_arr[i] *= h2rsqrt(var_reg); + } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + + sum = g.shfl(sum, 0); + sum /= (2 * row_stride); + __half2 sum_h = __float2half2_rn(sum); + + for (int i = 0; i < iterations; i++) { + __half2 xu_grad = ((-sum_h * xu[i] * h2rsqrt(var_reg)) / (var_reg)); + vals_arr_f[i] = __half22float2(vals_arr[i]); + float2 xu_grad_f = __half22float2(xu_grad); + vals_arr_f[i].x += xu_grad_f.x; + vals_arr_f[i].y += xu_grad_f.y; + } + + sum = 0.f; + for (int i = 0; i < iterations; i++) { + sum += (vals_arr_f[i].x); + sum += (vals_arr_f[i].y); + } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + + sum = g.shfl(sum, 0); + sum /= (2 * row_stride); + + for (int i = 0; i < iterations; i++) { + vals_arr_f[i].x -= sum; + vals_arr_f[i].y -= sum; + __half2 temp = __float22half2_rn(vals_arr_f[i]); + inp_grad_h[row * row_stride + i * iteration_stride + id] = temp; + } +} + +template <> +void launch_layerNorm_backward(const float* out_grad, + const float* X_data, + const float* vars, + const float* means, + const float* gamma, + float* gamma_grad, + float* betta_grad, + float* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2]) +{ + constexpr int threads = THREADS; + int batch = batch_size * sequence_length; + + dim3 grid_dim(hidden_dim / TILE_DIM); + dim3 block_dim(TILE_DIM, TILE_DIM); + + LayerNormBackward1<<>>( + out_grad, X_data, vars, means, gamma_grad, betta_grad, batch, hidden_dim); + + dim3 grid_dim2(batch); + dim3 block_dim2(threads); + + if (hidden_dim == 768) + LayerNormBackward2<768><<>>( + out_grad, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 512) + LayerNormBackward2<512><<>>( + out_grad, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 1024) + LayerNormBackward2<1024><<>>( + out_grad, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 1536) + LayerNormBackward2<1536><<>>( + out_grad, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 2048) + LayerNormBackward2<2048><<>>( + out_grad, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 2560) + LayerNormBackward2<2560><<>>( + out_grad, X_data, gamma, vars, means, inp_grad); + else + throw std::runtime_error("Unsupport hidden_dim."); +} + +template <> +void launch_layerNorm_backward<__half>(const __half* out_grad, + const __half* X_data, + const __half* vars, + const __half* means, + const __half* gamma, + __half* gamma_grad, + __half* betta_grad, + __half* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2]) +{ + constexpr int threads = THREADS; + int batch = batch_size * sequence_length; + + dim3 grid_dim(hidden_dim / TILE_DIM); + dim3 block_dim(TILE_DIM, TILE_DIM); + + LayerNormBackward1<__half><<>>( + out_grad, X_data, vars, means, gamma_grad, betta_grad, batch, hidden_dim); + + dim3 grid_dim2(batch); + dim3 block_dim2(threads / 2); + + if (hidden_dim == 768) + LayerNormBackward2<384><<>>( + out_grad, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 512) + LayerNormBackward2<256><<>>( + out_grad, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 1024) + LayerNormBackward2<512><<>>( + out_grad, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 1536) + LayerNormBackward2<768><<>>( + out_grad, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 2048) + LayerNormBackward2<1024><<>>( + out_grad, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 2560) + LayerNormBackward2<1280><<>>( + out_grad, X_data, gamma, vars, means, inp_grad); + else + throw std::runtime_error("Unsupport hidden_dim."); +} + +template +__global__ void LayerNormBackward1_fused_add(const T* __restrict__ out_grad1, + const T* __restrict__ out_grad2, + const T* __restrict__ vals_hat, + const T* __restrict__ gamma, + const T* __restrict__ betta, + T* __restrict__ gamma_grad, + T* __restrict__ betta_grad, + int rows, + int width, + bool invertible) +{ + __shared__ float betta_buffer[TILE_DIM][TILE_DIM + 1]; + __shared__ float gamma_buffer[TILE_DIM][TILE_DIM + 1]; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int idx = blockDim.x * blockIdx.x + threadIdx.x; + int offset = threadIdx.y * width + idx; + int y_stride = width * TILE_DIM; + + int pos = blockIdx.x * TILE_DIM + threadIdx.y; + float betta_reg = (invertible ? (float)betta[pos] : 0.0f); + float gamma_reg = (float)gamma[pos]; + + // Loop across matrix height + float betta_tmp = 0; + float gamma_tmp = 0; + for (int r = threadIdx.y; r < rows; r += TILE_DIM) { + float grad = (float)out_grad1[offset] + (float)out_grad2[offset]; + float val = (invertible ? ((float)vals_hat[offset] - betta_reg) / gamma_reg + : (float)vals_hat[offset]); + betta_tmp += grad; + gamma_tmp += (val * grad); + + offset += y_stride; + } + + betta_buffer[threadIdx.x][threadIdx.y] = betta_tmp; + gamma_buffer[threadIdx.x][threadIdx.y] = gamma_tmp; + + __syncthreads(); + + // Sum the shared buffer. + float s1 = betta_buffer[threadIdx.y][threadIdx.x]; + float s2 = gamma_buffer[threadIdx.y][threadIdx.x]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < TILE_DIM; i <<= 1) { + s1 += g.shfl_down(s1, i); + s2 += g.shfl_down(s2, i); + } + + if (threadIdx.x == 0) { + betta_grad[pos] = s1; + gamma_grad[pos] = s2; + } +} + +template +__global__ void LayerNormBackward1_fused_add(const T* __restrict__ out_grad1, + const T* __restrict__ out_grad2, + const T* __restrict__ X_data, + const T* __restrict__ vars, + const T* __restrict__ means, + T* __restrict__ gamma_grad, + T* __restrict__ betta_grad, + int rows, + int width) +{ + __shared__ float betta_buffer[TILE_DIM][TILE_DIM + 1]; + __shared__ float gamma_buffer[TILE_DIM][TILE_DIM + 1]; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int idx = blockDim.x * blockIdx.x + threadIdx.x; + int offset = threadIdx.y * width + idx; + int y_stride = width * TILE_DIM; + + int pos = blockIdx.x * TILE_DIM + threadIdx.y; + // Loop across matrix height + + float betta_tmp = 0; + float gamma_tmp = 0; + for (int r = threadIdx.y; r < rows; r += TILE_DIM) { + float grad = (float)out_grad1[offset] + (float)out_grad2[offset]; + float val = (float)X_data[offset]; + val = (val - (float)means[r]) * rsqrtf((float)vars[r]); + betta_tmp += grad; + gamma_tmp += (val * grad); + + offset += y_stride; + } + + betta_buffer[threadIdx.x][threadIdx.y] = betta_tmp; + gamma_buffer[threadIdx.x][threadIdx.y] = gamma_tmp; + + __syncthreads(); + + // Sum the shared buffer. + float s1 = betta_buffer[threadIdx.y][threadIdx.x]; + float s2 = gamma_buffer[threadIdx.y][threadIdx.x]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < TILE_DIM; i <<= 1) { + s1 += g.shfl_down(s1, i); + s2 += g.shfl_down(s2, i); + } + + if (threadIdx.x == 0) { + betta_grad[pos] = s1; + gamma_grad[pos] = s2; + } +} + +template // Hidden_Dim +__global__ void LayerNormBackward2_fused_add(const float* out_grad1, + const float* out_grad2, + const float* vals_hat, + const float* gamma, + const float* betta, + const float* vars, + float* inp_grad, + bool invertible) +{ + constexpr int iterations = row_stride / THREADS; + constexpr int iteration_stride = THREADS; // row_stride / iterations; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int row = blockIdx.x; + int id = threadIdx.x; + int wid = id / WARP_SIZE; + constexpr int warp_num = (THREADS < row_stride ? THREADS : row_stride) / WARP_SIZE; + __shared__ float partialSum[warp_num]; + + float vals_arr[iterations]; + float vals_hat_arr[iterations]; + +#pragma unroll + for (int i = 0; i < iterations; i++) { + float gamma_reg = gamma[i * iteration_stride + id]; + vals_arr[i] = out_grad1[row * row_stride + i * iteration_stride + id]; + vals_arr[i] *= gamma_reg; + vals_hat_arr[i] = (invertible ? (vals_hat[row * row_stride + i * iteration_stride + id] - + betta[i * iteration_stride + id]) / + gamma_reg + : vals_hat[row * row_stride + i * iteration_stride + id]); + } + + float var_reg = vars[row]; + + float sum = 0; + for (int i = 0; i < iterations; i++) { + sum += vals_hat_arr[i] * vals_arr[i] * sqrtf(var_reg); + vals_arr[i] *= rsqrtf(var_reg); + } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + + sum = g.shfl(sum, 0); + sum /= row_stride; + + for (int i = 0; i < iterations; i++) { vals_arr[i] += ((-sum * vals_hat_arr[i]) / var_reg); } + + sum = 0; + for (int i = 0; i < iterations; i++) { sum += vals_arr[i]; } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + sum = g.shfl(sum, 0); + sum /= row_stride; + + for (int i = 0; i < iterations; i++) + inp_grad[row * row_stride + i * iteration_stride + id] = + (vals_arr[i] - sum) + out_grad2[row * row_stride + i * iteration_stride + id]; +} + +template // Hidden_Dim +__global__ void LayerNormBackward2_fused_add(const __half* out_grad1, + const __half* out_grad2, + const __half* vals_hat, + const __half* gamma, + const __half* betta, + const __half* vars, + __half* inp_grad, + bool invertible) +{ + constexpr int iteration_stride = THREADS / 2; // row_stride / iterations; + constexpr int iterations = row_stride / iteration_stride; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int row = blockIdx.x; + int id = threadIdx.x; + int wid = id / WARP_SIZE; + constexpr int warp_num = + (iteration_stride < row_stride ? iteration_stride : row_stride) / WARP_SIZE; + __shared__ float partialSum[warp_num]; + + __half2 vals_arr[iterations]; + float2 vals_arr_f[iterations]; + __half2 vals_hat_arr[iterations]; + + // float2 result[iterations]; + + __half2* inp_grad_h = reinterpret_cast<__half2*>(inp_grad); + const __half2* out_grad_h1 = reinterpret_cast(out_grad1); + const __half2* out_grad_h2 = reinterpret_cast(out_grad2); + const __half2* vals_hat_h = reinterpret_cast(vals_hat); + + const __half2* gamma_h = reinterpret_cast(gamma); + const __half2* betta_h = (invertible ? reinterpret_cast(betta) : nullptr); + +#pragma unroll + for (int i = 0; i < iterations; i++) { + __half2 gamma_reg = gamma_h[i * iteration_stride + id]; + vals_arr[i] = out_grad_h1[row * row_stride + i * iteration_stride + id]; + vals_arr[i] *= gamma_reg; // out_grad * gamma + vals_hat_arr[i] = (invertible ? (vals_hat_h[row * row_stride + i * iteration_stride + id] - + betta_h[i * iteration_stride + id]) / + gamma_reg + : vals_hat_h[row * row_stride + i * iteration_stride + id]); + } + __half var_h = vars[row]; + __half2 var_reg = __halves2half2(var_h, var_h); + + float sum = 0.f; + for (int i = 0; i < iterations; i++) { + __half2 result_h = (vals_hat_arr[i] * vals_arr[i] * h2sqrt(var_reg)); + float2 result_f = __half22float2(result_h); + sum += result_f.x; + sum += result_f.y; + vals_arr[i] *= h2rsqrt(var_reg); + } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + + sum = g.shfl(sum, 0); + sum /= (2 * row_stride); + __half2 sum_h = __float2half2_rn(sum); + + for (int i = 0; i < iterations; i++) { + __half2 temp = ((-sum_h * vals_hat_arr[i]) / (var_reg)); + vals_arr_f[i] = __half22float2(vals_arr[i]); + float2 temp_f = __half22float2(temp); + vals_arr_f[i].x += temp_f.x; + vals_arr_f[i].y += temp_f.y; + } + sum = 0.f; + for (int i = 0; i < iterations; i++) { + sum += (vals_arr_f[i].x); + sum += (vals_arr_f[i].y); + } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + + sum = g.shfl(sum, 0); + sum /= (2 * row_stride); + + for (int i = 0; i < iterations; i++) { + vals_arr_f[i].x -= sum; + vals_arr_f[i].y -= sum; + __half2 temp = __float22half2_rn(vals_arr_f[i]); + + inp_grad_h[row * row_stride + i * iteration_stride + id] = + temp + out_grad_h2[row * row_stride + i * iteration_stride + id]; + } +} + +template <> +void launch_layerNorm_backward_fused_add(const float* out_grad1, + const float* out_grad2, + const float* vals_hat, + const float* vars, + const float* gamma, + float* gamma_grad, + float* betta_grad, + float* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2], + bool invertible, + const float* betta) +{ + constexpr int threads = THREADS; + int batch = batch_size * sequence_length; + + dim3 grid_dim(hidden_dim / TILE_DIM); + dim3 block_dim(TILE_DIM, TILE_DIM); + LayerNormBackward1<<>>( + out_grad1, vals_hat, gamma, betta, gamma_grad, betta_grad, batch, hidden_dim, invertible); + + dim3 grid_dim2(batch); + dim3 block_dim2(threads); + + if (hidden_dim == 768) + LayerNormBackward2_fused_add<768><<>>( + out_grad1, out_grad2, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 512) + LayerNormBackward2_fused_add<512><<>>( + out_grad1, out_grad2, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 1024) + LayerNormBackward2_fused_add<1024><<>>( + out_grad1, out_grad2, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 1536) + LayerNormBackward2_fused_add<1536><<>>( + out_grad1, out_grad2, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 2048) + LayerNormBackward2_fused_add<2048><<>>( + out_grad1, out_grad2, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 2560) + LayerNormBackward2_fused_add<2560><<>>( + out_grad1, out_grad2, vals_hat, gamma, betta, vars, inp_grad, invertible); + else + throw std::runtime_error("Unsupport hidden_dim."); +} + +template <> +void launch_layerNorm_backward_fused_add<__half>(const __half* out_grad1, + const __half* out_grad2, + const __half* vals_hat, + const __half* vars, + const __half* gamma, + __half* gamma_grad, + __half* betta_grad, + __half* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2], + bool invertible, + const __half* betta) +{ + constexpr int threads = THREADS; + int batch = batch_size * sequence_length; + + dim3 grid_dim(hidden_dim / TILE_DIM); + dim3 block_dim(TILE_DIM, TILE_DIM); + + LayerNormBackward1<__half><<>>( + out_grad1, vals_hat, gamma, betta, gamma_grad, betta_grad, batch, hidden_dim, invertible); + + dim3 grid_dim2(batch); + dim3 block_dim2(threads / 2); + + if (hidden_dim == 768) + LayerNormBackward2_fused_add<384><<>>( + out_grad1, out_grad2, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 512) + LayerNormBackward2_fused_add<256><<>>( + out_grad1, out_grad2, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 1024) + LayerNormBackward2_fused_add<512><<>>( + out_grad1, out_grad2, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 1536) + LayerNormBackward2_fused_add<768><<>>( + out_grad1, out_grad2, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 2048) + LayerNormBackward2_fused_add<1024><<>>( + out_grad1, out_grad2, vals_hat, gamma, betta, vars, inp_grad, invertible); + else if (hidden_dim == 2560) + LayerNormBackward2_fused_add<1280><<>>( + out_grad1, out_grad2, vals_hat, gamma, betta, vars, inp_grad, invertible); + else + throw std::runtime_error("Unsupport hidden_dim."); +} + +/* Backward Normalize (Input-Gradient) + * Using the means and variances from the input + * This type of backward is not invertible! + * We do the backward using the input (X) + */ + +template // Hidden_Dim +__global__ void LayerNormBackward2_fused_add(const float* out_grad1, + const float* out_grad2, + const float* X_vals, + const float* gamma, + const float* vars, + const float* means, + float* inp_grad) +{ + constexpr int iterations = row_stride / THREADS; + constexpr int iteration_stride = THREADS; // row_stride / iterations; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int row = blockIdx.x; + int id = threadIdx.x; + int wid = id / WARP_SIZE; + constexpr int warp_num = (THREADS < row_stride ? THREADS : row_stride) / WARP_SIZE; + __shared__ float partialSum[warp_num]; + + float vals_arr[iterations]; + float vals_hat_arr[iterations]; + +#pragma unroll + for (int i = 0; i < iterations; i++) { + float gamma_reg = gamma[i * iteration_stride + id]; + vals_arr[i] = out_grad1[row * row_stride + i * iteration_stride + id]; + vals_arr[i] *= gamma_reg; + vals_hat_arr[i] = X_vals[row * row_stride + i * iteration_stride + id]; + } + + float var_reg = vars[row]; + float mean_reg = means[row]; + + float sum = 0; + float xu[iterations]; + for (int i = 0; i < iterations; i++) { + xu[i] = (vals_hat_arr[i] - mean_reg); + sum += vals_arr[i] * xu[i]; + vals_arr[i] *= rsqrtf(var_reg); + } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + + sum = g.shfl(sum, 0); + sum /= row_stride; + + for (int i = 0; i < iterations; i++) { + vals_arr[i] += (-sum * xu[i] * rsqrtf(var_reg) / (var_reg)); + } + + sum = 0; + for (int i = 0; i < iterations; i++) { sum += vals_arr[i]; } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + sum = g.shfl(sum, 0); + sum /= row_stride; + + for (int i = 0; i < iterations; i++) + inp_grad[row * row_stride + i * iteration_stride + id] = + (vals_arr[i] - sum) + out_grad2[row * row_stride + i * iteration_stride + id]; + ; +} + +template // Hidden_Dim +__global__ void LayerNormBackward2_fused_add(const __half* out_grad1, + const __half* out_grad2, + const __half* X_vals, + const __half* gamma, + const __half* vars, + const __half* means, + __half* inp_grad) +{ + constexpr int iteration_stride = THREADS / 2; // row_stride / iterations; + constexpr int iterations = row_stride / iteration_stride; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int row = blockIdx.x; + int id = threadIdx.x; + int wid = id / WARP_SIZE; + constexpr int warp_num = + (iteration_stride < row_stride ? iteration_stride : row_stride) / WARP_SIZE; + + __shared__ float partialSum[warp_num]; + + __half2 vals_arr[iterations]; + float2 vals_arr_f[iterations]; + __half2 vals_hat_arr[iterations]; + + __half2* inp_grad_h = reinterpret_cast<__half2*>(inp_grad); + const __half2* out_grad_h1 = reinterpret_cast(out_grad1); + const __half2* out_grad_h2 = reinterpret_cast(out_grad2); + const __half2* vals_hat_h = reinterpret_cast(X_vals); + + const __half2* gamma_h = reinterpret_cast(gamma); + +#pragma unroll + for (int i = 0; i < iterations; i++) { + __half2 gamma_reg = gamma_h[i * iteration_stride + id]; + vals_arr[i] = out_grad_h1[row * row_stride + i * iteration_stride + id]; + vals_arr[i] *= gamma_reg; // out_grad * gamma + vals_hat_arr[i] = vals_hat_h[row * row_stride + i * iteration_stride + id]; + } + + __half mean_h = means[row]; + __half var_h = vars[row]; + __half2 var_reg = __halves2half2(var_h, var_h); + __half2 mean_reg = __halves2half2(mean_h, mean_h); + __half2 xu[iterations]; + + float sum = 0.f; + for (int i = 0; i < iterations; i++) { + xu[i] = (vals_hat_arr[i] - mean_reg); + __half2 result_h = (xu[i] * vals_arr[i]); + float2 result_f = __half22float2(result_h); + sum += result_f.x; + sum += result_f.y; + vals_arr[i] *= h2rsqrt(var_reg); + } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + + sum = g.shfl(sum, 0); + sum /= (2 * row_stride); + __half2 sum_h = __float2half2_rn(sum); + + for (int i = 0; i < iterations; i++) { + __half2 xu_grad = ((-sum_h * xu[i] * h2rsqrt(var_reg)) / (var_reg)); + vals_arr_f[i] = __half22float2(vals_arr[i]); + float2 xu_grad_f = __half22float2(xu_grad); + vals_arr_f[i].x += xu_grad_f.x; + vals_arr_f[i].y += xu_grad_f.y; + } + + sum = 0.f; + for (int i = 0; i < iterations; i++) { + sum += (vals_arr_f[i].x); + sum += (vals_arr_f[i].y); + } + + for (int i = 1; i < WARP_SIZE; i *= 2) { sum += g.shfl_down(sum, i); } + + if (g.thread_rank() == 0) partialSum[wid] = sum; + + __syncthreads(); + + if (g.thread_rank() < warp_num) sum = partialSum[g.thread_rank()]; + +#ifndef __STOCHASTIC_MODE__ + __syncthreads(); +#endif + + for (int i = 1; i < warp_num; i *= 2) sum += g.shfl_down(sum, i); + + sum = g.shfl(sum, 0); + sum /= (2 * row_stride); + + for (int i = 0; i < iterations; i++) { + vals_arr_f[i].x -= sum; + vals_arr_f[i].y -= sum; + __half2 temp = __float22half2_rn(vals_arr_f[i]); + inp_grad_h[row * row_stride + i * iteration_stride + id] = + temp + out_grad_h2[row * row_stride + i * iteration_stride + id]; + } +} + +template <> +void launch_layerNorm_backward_fused_add(const float* out_grad1, + const float* out_grad2, + const float* X_data, + const float* vars, + const float* means, + const float* gamma, + float* gamma_grad, + float* betta_grad, + float* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2]) +{ + constexpr int threads = THREADS; + int batch = batch_size * sequence_length; + + dim3 grid_dim(hidden_dim / TILE_DIM); + dim3 block_dim(TILE_DIM, TILE_DIM); + + LayerNormBackward1<<>>( + out_grad1, X_data, vars, means, gamma_grad, betta_grad, batch, hidden_dim); + + dim3 grid_dim2(batch); + dim3 block_dim2(threads); + + if (hidden_dim == 768) + LayerNormBackward2_fused_add<768><<>>( + out_grad1, out_grad2, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 512) + LayerNormBackward2_fused_add<512><<>>( + out_grad1, out_grad2, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 1024) + LayerNormBackward2_fused_add<1024><<>>( + out_grad1, out_grad2, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 1536) + LayerNormBackward2_fused_add<1536><<>>( + out_grad1, out_grad2, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 2048) + LayerNormBackward2_fused_add<2048><<>>( + out_grad1, out_grad2, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 2560) + LayerNormBackward2_fused_add<2560><<>>( + out_grad1, out_grad2, X_data, gamma, vars, means, inp_grad); + else + throw std::runtime_error("Unsupport hidden_dim."); +} + +template <> +void launch_layerNorm_backward_fused_add<__half>(const __half* out_grad1, + const __half* out_grad2, + const __half* X_data, + const __half* vars, + const __half* means, + const __half* gamma, + __half* gamma_grad, + __half* betta_grad, + __half* inp_grad, + int batch_size, + int sequence_length, + int hidden_dim, + cudaStream_t stream[2]) +{ + constexpr int threads = THREADS; + int batch = batch_size * sequence_length; + + dim3 grid_dim(hidden_dim / TILE_DIM); + dim3 block_dim(TILE_DIM, TILE_DIM); + + LayerNormBackward1<__half><<>>( + out_grad1, X_data, vars, means, gamma_grad, betta_grad, batch, hidden_dim); + + dim3 grid_dim2(batch); + dim3 block_dim2(threads / 2); + + if (hidden_dim == 768) + LayerNormBackward2_fused_add<384><<>>( + out_grad1, out_grad2, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 512) + LayerNormBackward2_fused_add<256><<>>( + out_grad1, out_grad2, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 1024) + LayerNormBackward2_fused_add<512><<>>( + out_grad1, out_grad2, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 1536) + LayerNormBackward2_fused_add<768><<>>( + out_grad1, out_grad2, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 2048) + LayerNormBackward2_fused_add<1024><<>>( + out_grad1, out_grad2, X_data, gamma, vars, means, inp_grad); + else if (hidden_dim == 2560) + LayerNormBackward2_fused_add<1280><<>>( + out_grad1, out_grad2, X_data, gamma, vars, means, inp_grad); + else + throw std::runtime_error("Unsupport hidden_dim."); +} diff --git a/csrc/transformer/softmax_kernels.cu b/csrc/transformer/softmax_kernels.cu new file mode 100644 index 000000000000..8e2b86901609 --- /dev/null +++ b/csrc/transformer/softmax_kernels.cu @@ -0,0 +1,596 @@ +#include "custom_cuda_layers.h" +#include "general_kernels.h" + +namespace cg = cooperative_groups; + +// Fused attention + softmax +template +__global__ void attn_softmax(float* vals, + const float* attn_mask, + int heads, + int seq_length, + int iterations) +{ + __shared__ float partialSum[MAX_WARP_NUM]; + + int warp_num = blockDim.x >> 5; + + int iteration_stride = blockDim.x; + int block_width = blockStride * seq_length; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int batch = blockIdx.x; + int row = blockIdx.y; + int max_threads_in_sequence = std::max(seq_length, tbSeq); + int seq_lane = threadIdx.x % max_threads_in_sequence; + + int data_offset = batch * (gridDim.y * block_width) + row * block_width + + (threadIdx.x / max_threads_in_sequence) * seq_length; + int mask_offset = batch * seq_length; + + int wid = threadIdx.x >> 5; + int lane = threadIdx.x & 0x1f; + + float4* val_cast = reinterpret_cast(vals); + const float4* attn_mask_cast = reinterpret_cast(attn_mask); + + float4 data[MAX_THREAD_ITERATIONS]; + + float max_val = minus_infinity; + + for (int i = 0; i < iterations; i++) { + int data_id = i * iteration_stride + seq_lane; + if (data_id < seq_length) { + float4 mask = attn_mask_cast[mask_offset + data_id]; + data[i] = val_cast[data_offset + data_id]; + + data[i].x += mask.x; + data[i].y += mask.y; + data[i].z += mask.z; + data[i].w += mask.w; + + max_val = (data[i].x > max_val ? data[i].x : max_val); + max_val = (data[i].y > max_val ? data[i].y : max_val); + max_val = (data[i].z > max_val ? data[i].z : max_val); + max_val = (data[i].w > max_val ? data[i].w : max_val); + } else { + data[i].x = minus_infinity; + data[i].y = minus_infinity; + data[i].z = minus_infinity; + data[i].w = minus_infinity; + } + } + + for (int i = 1; i < tbSize; i *= 2) { + auto temp = g.shfl_xor(max_val, i); + max_val = (temp > max_val ? temp : max_val); + } + + if (seq_length > tbSize) { + if (lane == 0) partialSum[wid] = max_val; + b.sync(); + + if (lane < warp_num) max_val = partialSum[lane]; + +#ifndef __STOCHASTIC_MODE__ + b.sync(); +#endif + + int iters = warp_num; + if (seq_length < iteration_stride) iters = warp_num / (iteration_stride / seq_length); + + for (int i = 1; i < iters; i *= 2) { + auto temp = g.shfl_xor(max_val, i); + max_val = (temp > max_val ? temp : max_val); + } + + max_val = g.shfl(max_val, threadIdx.x / tbSize); + } + + float sum = 0; + for (int i = 0; i < iterations; i++) { + data[i].x = __expf(data[i].x - max_val); + data[i].y = __expf(data[i].y - max_val); + data[i].z = __expf(data[i].z - max_val); + data[i].w = __expf(data[i].w - max_val); + + sum += (data[i].x + data[i].y + data[i].z + data[i].w); + } + + for (int i = 1; i < tbSize; i *= 2) { sum += g.shfl_xor(sum, i); } + + if (seq_length > tbSize) { + if (lane == 0) partialSum[wid] = sum; + b.sync(); + + if (lane < warp_num) sum = partialSum[lane]; + +#ifndef __STOCHASTIC_MODE__ + b.sync(); +#endif + + int iters = warp_num; + if (seq_length < iteration_stride) iters = warp_num / (iteration_stride / seq_length); + + for (int i = 1; i < iters; i *= 2) { sum += g.shfl_xor(sum, i); } + + sum = g.shfl(sum, threadIdx.x / tbSize); + } + + sum += 1e-6; + + for (int i = 0; i < iterations; i++) { + data[i].x /= sum; + data[i].y /= sum; + data[i].z /= sum; + data[i].w /= sum; + + int data_id = i * iteration_stride + seq_lane; + if (data_id < seq_length) val_cast[data_offset + data_id] = data[i]; + } +} + +template +__global__ void attn_softmax(__half* vals, + const __half* attn_mask, + int heads, + int seq_length, + int iterations) +{ +#if __CUDA_ARCH__ >= 700 + __shared__ float partialSum[MAX_WARP_NUM]; + + int warp_num = blockDim.x >> 5; + + int iteration_stride = blockDim.x; + int block_width = blockStride * seq_length; + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int batch = blockIdx.x; + int row = blockIdx.y; + int max_threads_in_sequence = std::max(seq_length, tbSeq); + int seq_lane = threadIdx.x % max_threads_in_sequence; + + int data_offset = batch * (gridDim.y * block_width) + row * block_width + + (threadIdx.x / max_threads_in_sequence) * seq_length; + int mask_offset = batch * seq_length; + + int wid = threadIdx.x >> 5; + int lane = threadIdx.x & 0x1f; + + float2* val_cast = reinterpret_cast(vals); + const float2* attn_mask_cast = reinterpret_cast(attn_mask); + + val_cast += data_offset; + attn_mask_cast += mask_offset; + + float2 low_data[MAX_THREAD_ITERATIONS]; + float2 high_data[MAX_THREAD_ITERATIONS]; + + float max_val = minus_infinity; + + for (int i = 0; i < iterations; i++) { + int data_id = i * iteration_stride + seq_lane; + if (data_id < seq_length) { + float2 data = val_cast[data_id]; + float2 mask = attn_mask_cast[data_id]; + + __half2* data_arr = reinterpret_cast<__half2*>(&data); + __half2* mask_arr = reinterpret_cast<__half2*>(&mask); + + low_data[i] = __half22float2(data_arr[0]); + high_data[i] = __half22float2(data_arr[1]); + float2 low_mask = __half22float2(mask_arr[0]); + float2 high_mask = __half22float2(mask_arr[1]); + + low_data[i].x += low_mask.x; + low_data[i].y += low_mask.y; + high_data[i].x += high_mask.x; + high_data[i].y += high_mask.y; + + max_val = (low_data[i].x > max_val ? low_data[i].x : max_val); + max_val = (low_data[i].y > max_val ? low_data[i].y : max_val); + max_val = (high_data[i].x > max_val ? high_data[i].x : max_val); + max_val = (high_data[i].y > max_val ? high_data[i].y : max_val); + } + } + + for (int i = 1; i < tbSize; i *= 2) { + auto temp = g.shfl_xor(max_val, i); + max_val = (temp > max_val ? temp : max_val); + } + + if (seq_length > tbSize) { + if (lane == 0) partialSum[wid] = max_val; + b.sync(); + + if (lane < warp_num) max_val = partialSum[lane]; + +#ifndef __STOCHASTIC_MODE__ + b.sync(); +#endif + + int iters = warp_num; + if (seq_length < iteration_stride) iters = warp_num / (iteration_stride / seq_length); + + for (int i = 1; i < iters; i *= 2) { + auto temp = g.shfl_xor(max_val, i); + max_val = (temp > max_val ? temp : max_val); + } + + max_val = g.shfl(max_val, threadIdx.x / tbSize); + } + + float sum = 0; + for (int i = 0; i < iterations; i++) { + int data_id = i * iteration_stride + seq_lane; + if (data_id < seq_length) { + low_data[i].x = __expf(low_data[i].x - max_val); + low_data[i].y = __expf(low_data[i].y - max_val); + high_data[i].x = __expf(high_data[i].x - max_val); + high_data[i].y = __expf(high_data[i].y - max_val); + + sum += (low_data[i].x + low_data[i].y + high_data[i].x + high_data[i].y); + } + } + + for (int i = 1; i < tbSize; i *= 2) { sum += g.shfl_xor(sum, i); } + + if (seq_length > tbSize) { + if (lane == 0) partialSum[wid] = sum; + b.sync(); + + if (lane < warp_num) sum = partialSum[lane]; + +#ifndef __STOCHASTIC_MODE__ + b.sync(); +#endif + + int iters = warp_num; + if (seq_length < iteration_stride) iters = warp_num / (iteration_stride / seq_length); + + for (int i = 1; i < iters; i *= 2) { sum += g.shfl_xor(sum, i); } + + sum = g.shfl(sum, threadIdx.x / tbSize); + } + + sum += 1e-6; + + for (int i = 0; i < iterations; i++) { + int data_id = i * iteration_stride + seq_lane; + if (data_id < seq_length) { + float2 result_f; + __half2* result_h = reinterpret_cast<__half2*>(&result_f); + + low_data[i].x /= sum; + low_data[i].y /= sum; + high_data[i].x /= sum; + high_data[i].y /= sum; + + result_h[0] = __float22half2_rn(low_data[i]); + result_h[1] = __float22half2_rn(high_data[i]); + + val_cast[data_id] = result_f; + } + } + +#endif +} + +template +void launch_attn_softmax(T*, const T*, int, int, int, cudaStream_t, bool); + +template <> +void launch_attn_softmax(float* vals, + const float* attn_mask, + int batch_size, + int heads, + int sequence_length, + cudaStream_t stream) +{ + const int threads = 128; + int seq_length4 = sequence_length / 4; + int seq2 = sequence_length * seq_length4; + + int block_compute_size = + (seq_length4 < threads ? ((threads / seq_length4) * seq_length4) : seq_length4); + dim3 grid_dim(batch_size, heads * seq2 / block_compute_size); + + int subblock_max_workload = MAX_THREAD_ITERATIONS * 4 * threads; + + dim3 block_dim(seq_length4 > threads ? ((sequence_length + subblock_max_workload - 1) / + subblock_max_workload * threads) + : threads); + int iterations = + (sequence_length < subblock_max_workload ? (seq_length4 + threads - 1) / threads + : MAX_THREAD_ITERATIONS); + + if (sequence_length <= 8) + attn_softmax<2, (threads / 2), 2> + <<>>(vals, attn_mask, heads, seq_length4, iterations); + else if (sequence_length <= 16) + attn_softmax<4, (threads / 4), 4> + <<>>(vals, attn_mask, heads, seq_length4, iterations); + else if (sequence_length <= 32) + attn_softmax<8, (threads / 8), 8> + <<>>(vals, attn_mask, heads, seq_length4, iterations); + else if (sequence_length <= 64) + attn_softmax<16, (threads / 16), 16> + <<>>(vals, attn_mask, heads, seq_length4, iterations); + else if (sequence_length <= 128) + attn_softmax<32, (threads / 32), 32> + <<>>(vals, attn_mask, heads, seq_length4, iterations); + else if (sequence_length <= 256) + attn_softmax<32, (threads / 64), 64> + <<>>(vals, attn_mask, heads, seq_length4, iterations); + else { + const int threads = 256; + block_compute_size = + (seq_length4 < threads ? ((threads / seq_length4) * seq_length4) : seq_length4); + dim3 grid_dim(batch_size, heads * seq2 / block_compute_size); + + int subblock_max_workload = MAX_THREAD_ITERATIONS * 4 * threads; + + dim3 block_dim(seq_length4 > threads ? ((sequence_length + subblock_max_workload - 1) / + subblock_max_workload * threads) + : threads); + + if (sequence_length <= 512) + attn_softmax<32, (threads / 128), 128><<>>( + vals, attn_mask, heads, seq_length4, iterations); + else if (sequence_length < (MAX_THREADS * MAX_THREAD_ITERATIONS * 4)) + attn_softmax<32, 1, 128><<>>( + vals, attn_mask, heads, seq_length4, iterations); + else + throw std::runtime_error( + "Unsupport Seq_Length! Check the restriction of the max_threads and " + "max_thread_iterations!"); + } +} + +template <> +void launch_attn_softmax<__half>(__half* vals, + const __half* attn_mask, + int batch_size, + int heads, + int sequence_length, + cudaStream_t stream) +{ + const int threads = 128; + int seq_length4 = sequence_length / 4; + int seq2 = sequence_length * seq_length4; + + int block_compute_size = + (seq_length4 < threads ? ((threads / seq_length4) * seq_length4) : seq_length4); + dim3 grid_dim(batch_size, heads * seq2 / block_compute_size); + + int subblock_max_workload = MAX_THREAD_ITERATIONS * 4 * threads; + + dim3 block_dim(seq_length4 > threads ? ((sequence_length + subblock_max_workload - 1) / + subblock_max_workload * threads) + : threads); + + int iterations = + (sequence_length < subblock_max_workload ? (seq_length4 + threads - 1) / threads + : MAX_THREAD_ITERATIONS); + + if (sequence_length <= 8) + attn_softmax<2, (threads / 2), 2> + <<>>(vals, attn_mask, heads, seq_length4, iterations); + else if (sequence_length <= 16) + attn_softmax<4, (threads / 4), 4> + <<>>(vals, attn_mask, heads, seq_length4, iterations); + else if (sequence_length <= 32) + attn_softmax<8, (threads / 8), 8> + <<>>(vals, attn_mask, heads, seq_length4, iterations); + else if (sequence_length <= 64) + attn_softmax<16, (threads / 16), 16> + <<>>(vals, attn_mask, heads, seq_length4, iterations); + else if (sequence_length <= 128) + attn_softmax<32, (threads / 32), 32> + <<>>(vals, attn_mask, heads, seq_length4, iterations); + else if (sequence_length <= 256) + attn_softmax<32, (threads / 64), 64> + <<>>(vals, attn_mask, heads, seq_length4, iterations); + else { + const int threads = 256; + block_compute_size = + (seq_length4 < threads ? ((threads / seq_length4) * seq_length4) : seq_length4); + dim3 grid_dim(batch_size, heads * seq2 / block_compute_size); + + int subblock_max_workload = MAX_THREAD_ITERATIONS * 4 * threads; + + dim3 block_dim(seq_length4 > threads ? ((sequence_length + subblock_max_workload - 1) / + subblock_max_workload * threads) + : threads); + + if (sequence_length <= 512) + attn_softmax<32, (threads / 128), 128><<>>( + vals, attn_mask, heads, seq_length4, iterations); + else if (sequence_length < (MAX_THREADS * MAX_THREAD_ITERATIONS * 4)) + attn_softmax<32, 1, 128><<>>( + vals, attn_mask, heads, seq_length4, iterations); + else + throw std::runtime_error( + "Unsupport Seq_Length! Check the restriction of the max_threads and " + "max_thread_iterations!"); + } +} + +template +__global__ void softmax_backward_kernel(T* out_grad, const T* soft_inp, int seq_length) +{ + __shared__ float partialSum[MAX_WARP_NUM]; + + int warp_num = blockDim.x >> 5; // warp-count = num_threads / WARP_SIZE (32) + + int iteration_stride = blockDim.x; + int block_width = blockStride * seq_length; + + int iterations = (seq_length < (MAX_THREAD_ITERATIONS * iteration_stride) + ? (seq_length + iteration_stride - 1) / iteration_stride + : MAX_THREAD_ITERATIONS); + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + int row = blockIdx.x; + int id = threadIdx.x; + + int wid = id >> 5; + int lane = id & 0x1f; + + T val_reg[MAX_THREAD_ITERATIONS]; + T soft_reg[MAX_THREAD_ITERATIONS]; + float grad_reg = 0.0f; + +#pragma unroll + for (int i = 0; i < iterations; i++) { + int data_id = i * iteration_stride + id; + if (data_id < block_width) { + val_reg[i] = out_grad[row * block_width + data_id]; + soft_reg[i] = soft_inp[row * block_width + data_id]; + + grad_reg += ((float)val_reg[i] * + (float)soft_reg[i]); // if done in half, the multiplication, we may lose + // 2% of accuracy in computation!! + } + } + for (int i = 1; i < tbSize; i *= 2) grad_reg += g.shfl_xor(grad_reg, i); + + if (seq_length > tbSize) { + if (lane == 0) partialSum[wid] = grad_reg; + b.sync(); + + if (lane < warp_num) grad_reg = partialSum[lane]; + + int iters = warp_num; + if (seq_length < iteration_stride) iters = warp_num / (iteration_stride / seq_length); + + for (int i = 1; i < iters; i *= 2) grad_reg += g.shfl_xor(grad_reg, i); + + grad_reg = g.shfl(grad_reg, id / tbSize); + } + + for (int i = 0; i < iterations; i++) { + int data_id = i * iteration_stride + id; + if (data_id < block_width) { + float temp = (float)soft_reg[i] * ((float)val_reg[i] - grad_reg); + out_grad[row * block_width + data_id] = (T)temp; + } + } +} + +template +__global__ void softmax_backward_kernel_v2(T* grad /* input & output*/, + const T* output, + int softmax_length) +{ + int batch_idx = blockIdx.x * blockDim.y + threadIdx.y; + int offset = batch_idx * softmax_length + threadIdx.x; + + grad += offset; + output += offset; + + T grad_reg[ITERATIONS]; + T output_reg[ITERATIONS]; + float sum = 0.0; + +#pragma unroll + for (int i = 0; i < ITERATIONS; ++i) { + int curr_idx = threadIdx.x + i * WARP_SIZE; + if (curr_idx < softmax_length) { + grad_reg[i] = grad[i * WARP_SIZE]; + output_reg[i] = output[i * WARP_SIZE]; + sum += (float)grad_reg[i] * (float)output_reg[i]; + } + } + + cg::thread_block b = cg::this_thread_block(); + cg::thread_block_tile g = cg::tiled_partition(b); + + for (int i = 1; i < WARP_SIZE; i <<= 1) sum += g.shfl_xor(sum, i); + +#pragma unroll + for (int i = 0; i < ITERATIONS; ++i) { + int curr_idx = threadIdx.x + i * WARP_SIZE; + if (curr_idx < softmax_length) + grad[i * WARP_SIZE] = (float)output_reg[i] * ((float)grad_reg[i] - sum); + } +} + +template +void launch_attn_softmax_backward_v2(T* out_grad, + const T* soft_inp, + int batch_size, + int heads, + int seq_length, + cudaStream_t stream) +{ + if ((seq_length % WARP_SIZE) != 0 || seq_length > 2048) + throw std::runtime_error("Invalid sequence length found in softmax backward."); + + const int warps_per_block = 4; + dim3 grid_dim(batch_size * heads * seq_length / warps_per_block); + dim3 block_dim(WARP_SIZE, warps_per_block); + + switch (seq_length) { + case 32: + softmax_backward_kernel_v2 + <<>>(out_grad, soft_inp, seq_length); + break; + case 64: + softmax_backward_kernel_v2 + <<>>(out_grad, soft_inp, seq_length); + break; + case 128: + softmax_backward_kernel_v2 + <<>>(out_grad, soft_inp, seq_length); + break; + case 256: + softmax_backward_kernel_v2 + <<>>(out_grad, soft_inp, seq_length); + break; + case 384: + softmax_backward_kernel_v2 + <<>>(out_grad, soft_inp, seq_length); + break; + case 512: + softmax_backward_kernel_v2 + <<>>(out_grad, soft_inp, seq_length); + break; + case 768: + softmax_backward_kernel_v2 + <<>>(out_grad, soft_inp, seq_length); + break; + case 1024: + softmax_backward_kernel_v2 + <<>>(out_grad, soft_inp, seq_length); + break; + case 2048: + softmax_backward_kernel_v2 + <<>>(out_grad, soft_inp, seq_length); + break; + default: + throw std::runtime_error( + std::string("Special sequence length found in softmax backward, seq_length: ") + + std::to_string(seq_length)); + } +} + +template void launch_attn_softmax_backward_v2<__half>(__half* out_grad, + const __half* soft_inp, + int batch_size, + int heads, + int seq_length, + cudaStream_t stream); +template void launch_attn_softmax_backward_v2(float* out_grad, + const float* soft_inp, + int batch_size, + int heads, + int seq_length, + cudaStream_t stream); diff --git a/csrc/transformer/transform_kernels.cu b/csrc/transformer/transform_kernels.cu new file mode 100644 index 000000000000..33422601cbbd --- /dev/null +++ b/csrc/transformer/transform_kernels.cu @@ -0,0 +1,418 @@ +#include "custom_cuda_layers.h" + +#define rows_trans 16 +#define cols_trans 16 + +template +__global__ void Transpose_Kernel(const T* inp, T* out, int row_width, int col_width) +{ + __shared__ T data_block[rows_trans * (cols_trans + 1)]; + + int r = threadIdx.x / cols_trans; + int c = threadIdx.x % cols_trans; + + int m = row_width / cols_trans; + + int i = blockIdx.x / m * rows_trans + r; + int j = blockIdx.x % m * cols_trans + c; + + int row_stride = rows_trans / ((rows_trans * cols_trans + THREADS - 1) / THREADS); + + for (int k = 0; k < rows_trans; k += row_stride) + data_block[(k + r) * cols_trans + c] = inp[(i + k) * row_width + j]; + + __syncthreads(); + + i = blockIdx.x % m * rows_trans + r; + j = blockIdx.x / m * cols_trans + c; + + for (int k = 0; k < rows_trans; k += row_stride) + out[(i + k) * col_width + j] = data_block[c * cols_trans + r + k]; +} + +template <> +void Transpose<__half>(const __half* inp_mat, + __half* out_mat, + int rows, + int cols, + cudaStream_t stream) +{ + int threads = THREADS; + + Transpose_Kernel<__half><<<(rows * cols + threads - 1) / threads, threads, 0, stream>>>( + inp_mat, out_mat, cols, rows); +} + +template <> +void Transpose(const float* inp_mat, float* out_mat, int rows, int cols, cudaStream_t stream) +{ + int threads = THREADS; + + Transpose_Kernel<<<(rows * cols + threads - 1) / threads, threads, 0, stream>>>( + inp_mat, out_mat, cols, rows); +} + +template +__global__ void transform_0213(T* output, const T* vals, int hidden_dim, int seq_length, int heads); + +template <> +__global__ void transform_0213(float* output, + const float* vals, + int hidden_dim, + int seq_length, + int heads) +{ + int d0_stride = hidden_dim * seq_length / 4; + int d1_stride = hidden_dim / 4; + int d2_stride = hidden_dim / heads / 4; + + int d0_out_stride = d0_stride; + int d1_out_stride = d2_stride; + int d2_out_stride = d2_stride * seq_length; + + int d0 = blockIdx.x; // Batch + int d1 = blockIdx.y; // Sequence ID (0-127) + int d2 = threadIdx.y; // Head (0-11) + int d3 = threadIdx.x; // Values (groups of 4) + + const float4* vals_vec = reinterpret_cast(vals); + float4* output_vec = reinterpret_cast(output); + + float4 inputs = vals_vec[d0 * d0_stride + d1 * d1_stride + d2 * d2_stride + d3]; + output_vec[d0 * d0_out_stride + d1 * d1_out_stride + d2 * d2_out_stride + d3] = inputs; +} + +template <> +__global__ void transform_0213<__half>(__half* output, + const __half* vals, + int hidden_dim, + int seq_length, + int heads) +{ +#if __CUDA_ARCH__ >= 700 + + int d0_stride = hidden_dim * seq_length / 8; + int d1_stride = hidden_dim / 8; + int d2_stride = hidden_dim / heads / 8; + + int d0_out_stride = d0_stride; + int d1_out_stride = d2_stride; + int d2_out_stride = d2_stride * seq_length; + + int d0 = blockIdx.x; // Batch + int d1 = blockIdx.y; // Sequence ID (0-127) + int d2 = threadIdx.y; // Head (0-11) + int d3 = threadIdx.x; // Values (groups of 4) + + float4 vals_arr[1]; + + const float4* vals_vec = reinterpret_cast(vals); + float4* output_vec = reinterpret_cast(output); + + vals_arr[0] = vals_vec[d0 * d0_stride + d1 * d1_stride + d2 * d2_stride + d3]; + output_vec[d0 * d0_out_stride + d1 * d1_out_stride + d2 * d2_out_stride + d3] = vals_arr[0]; +#endif +} + +template <> +void launch_transform_0213(float* output, + const float* vals, + int batch_size, + int seq_length, + int hidden_dim, + int heads, + cudaStream_t stream) +{ + dim3 block_dim(hidden_dim / heads / 4, heads); + dim3 grid_dim(batch_size, seq_length); + transform_0213 + <<>>(output, vals, hidden_dim, seq_length, heads); +} + +template <> +void launch_transform_0213<__half>(__half* output, + const __half* vals, + int batch_size, + int seq_length, + int hidden_dim, + int heads, + cudaStream_t stream) +{ + dim3 block_dim(hidden_dim / heads / 8, heads); + dim3 grid_dim(batch_size, seq_length); + transform_0213<__half> + <<>>(output, vals, hidden_dim, seq_length, heads); +} + +// Bias add +template +__global__ void bias_add_transform_0213(T* output, + const T* vals, + const T* bias, + int hidden_dim, + int seq_length, + int heads); + +template <> +__global__ void bias_add_transform_0213(float* output, + const float* vals, + const float* bias, + int hidden_dim, + int seq_length, + int heads) +{ + int d0_stride = hidden_dim * seq_length / 4; + int d1_stride = hidden_dim / 4; + int d2_stride = hidden_dim / heads / 4; + + int d0_out_stride = d0_stride; + int d1_out_stride = d2_stride; + int d2_out_stride = d2_stride * seq_length; + + int d0 = blockIdx.x; // Batch + int d1 = blockIdx.y; // Sequence ID (0-127) + int cnt = blockIdx.z; // Hidden count + int d2 = threadIdx.y; // Head (0-11) + int d3 = threadIdx.x; // Values (groups of 4) + + const float4* vals_vec = reinterpret_cast(vals); + const float4* bias_vec = reinterpret_cast(bias); + float4* output_vec = reinterpret_cast(output); + + float4 inputs = vals_vec[d0 * d0_stride * gridDim.z + cnt * d1_stride + + d1 * d1_stride * gridDim.z + d2 * d2_stride + d3]; + float4 biases = bias_vec[cnt * d1_stride + d2 * d2_stride + d3]; + + float4 outputs; + outputs.x = inputs.x + biases.x; + outputs.y = inputs.y + biases.y; + outputs.z = inputs.z + biases.z; + outputs.w = inputs.w + biases.w; + + output_vec[cnt * d0_out_stride * gridDim.x + d0 * d0_out_stride + d1 * d1_out_stride + + d2 * d2_out_stride + d3] = outputs; +} + +#define ATTN_H 3 +#define MAX_SEQ_LINE 10 + +template <> +__global__ void bias_add_transform_0213<__half>(__half* output, + const __half* vals, + const __half* bias, + int hidden_dim, + int seq_length, + int heads) +{ +#if __CUDA_ARCH__ >= 700 + __shared__ float4 in_data[3072]; + + int d0_stride = hidden_dim * seq_length / 8; + int d1_stride = hidden_dim / 8; + int d2_stride = hidden_dim / heads / 8; + int iteration_stride = d1_stride * blockDim.z; // Hidden * 3 / 8 + int batch_stride = d0_stride * blockDim.z; // Hidden * S * 3 / 8 + + int d0_out_stride = d0_stride; + int d1_out_stride = d2_stride; + int d2_out_stride = d2_stride * seq_length; + + int d0 = blockIdx.x; // Batch + int d1 = blockIdx.y; // Sequence ID (0-127) + int cnt = threadIdx.z; // blockIdx.z; // Hidden count + int d2 = threadIdx.y; // Head (0-11) + int d3 = threadIdx.x; // Values (groups of 4) + + float4 vals_arr[1]; + float4 bias_arr[1]; + float4 output_arr[1]; + __half2* vals_half = reinterpret_cast<__half2*>(vals_arr); + __half2* bias_half = reinterpret_cast<__half2*>(bias_arr); + __half2* output_half = reinterpret_cast<__half2*>(output_arr); + + const float4* vals_vec = reinterpret_cast(vals); + const float4* bias_vec = reinterpret_cast(bias); + float4* output_vec = reinterpret_cast(output); + + int iter_index = cnt * d1_stride + d2 * d2_stride + d3; + int input_offset = d0 * batch_stride + d1 * (iteration_stride << 1); + bias_arr[0] = bias_vec[iter_index]; + for (int iter = 0; iter < 2; iter++) { + int iter_id = iter * iteration_stride + iter_index; + vals_arr[0] = vals_vec[input_offset + iter_id]; + + output_half[0] = vals_half[0] + bias_half[0]; + output_half[1] = vals_half[1] + bias_half[1]; + output_half[2] = vals_half[2] + bias_half[2]; + output_half[3] = vals_half[3] + bias_half[3]; + + in_data[iter_id] = output_arr[0]; + } + __syncthreads(); + + iteration_stride = blockDim.z * (blockDim.y >> 1); + int matrix_stride = (d0_out_stride * gridDim.x); + int head_count = (d2 >> 1) + cnt * (blockDim.y >> 1); + + int out_index = d0 * d0_out_stride + d1 * (d1_out_stride << 1) + d3 + (d2 % 2) * d2_stride; + for (int iter = 0; iter < 2; iter++) { + int iter_row = (iter * iteration_stride) + head_count; + int iter_offset = + (iter_row % blockDim.y) * d2_out_stride + (iter_row / blockDim.y) * matrix_stride; + output_vec[out_index + iter_offset] = + in_data[iter_row * d2_stride + d3 + (d2 % 2) * (d1_stride * blockDim.z)]; + } +#endif +} + +// [B S C*H] - > C * [B A S N] +template <> +void launch_bias_add_transform_0213(float* output, + const float* vals, + const float* bias, + int batch_size, + int seq_length, + int hidden_dim, + int heads, + cudaStream_t stream, + int trans_count) +{ + dim3 block_dim(hidden_dim / heads / 4, heads); + dim3 grid_dim(batch_size, seq_length, trans_count); + bias_add_transform_0213 + <<>>(output, vals, bias, hidden_dim, seq_length, heads); +} + +template <> +void launch_bias_add_transform_0213<__half>(__half* output, + const __half* vals, + const __half* bias, + int batch_size, + int seq_length, + int hidden_dim, + int heads, + cudaStream_t stream, + int trans_count) +{ + dim3 block_dim(hidden_dim / heads / 8, heads, trans_count); + dim3 grid_dim(batch_size, seq_length / 2); + bias_add_transform_0213<__half> + <<>>(output, vals, bias, hidden_dim, seq_length, heads); +} + +template +__global__ void transform4d_0213(T* out, const T* in, int heads, int seq_length, int hidden_dim); + +template <> +__global__ void transform4d_0213(float* out, + const float* in, + int heads, + int seq_length, + int hidden_dim) +{ + int d0_stride = hidden_dim * seq_length / 4; + int d1_stride = d0_stride / heads; + int d2_stride = hidden_dim / heads / 4; + + int d0_out_stride = d0_stride; + int d1_out_stride = d2_stride; + int d2_out_stride = hidden_dim / 4; + + int d0 = blockIdx.x; // Batch + int d1 = blockIdx.y / ((seq_length + blockDim.y - 1) / blockDim.y); // Head + int d2 = (threadIdx.y + blockDim.y * blockIdx.y) % seq_length; + int cnt = blockIdx.z; + int d3 = threadIdx.x; // Values (groups of 8) + + if (d2 < seq_length) { + const float4* in_vec = reinterpret_cast(in); + float4* out_vec = reinterpret_cast(out); + + float4 vals_vec = in_vec[cnt * d0_stride * gridDim.x + d0 * d0_stride + d1 * d1_stride + + d2 * d2_stride + d3]; + out_vec[d0 * d0_out_stride * gridDim.z + cnt * d2_out_stride + d1 * d1_out_stride + + d2 * d2_out_stride * gridDim.z + d3] = vals_vec; + } +} + +template <> +__global__ void transform4d_0213<__half>(__half* out, + const __half* in, + int heads, + int seq_length, + int hidden_dim) +{ +#if __CUDA_ARCH__ >= 700 + __shared__ float4 in_data[3072]; + + int d0_stride = hidden_dim * seq_length / 8; + int d1_stride = hidden_dim / 8; + int d2_stride = hidden_dim / heads / 8; + + int d0 = blockIdx.x; // Batch + int d1 = threadIdx.y; // Head + int d2 = blockIdx.y; // Sequence + int cnt = threadIdx.z; // Hidden count + int d3 = threadIdx.x; // Values (groups of 8) + + const float4* in_vec = reinterpret_cast(in); + float4* out_vec = reinterpret_cast(out); + + int input_offset = d0 * d0_stride + d2 * (d2_stride << 1) + d3 + d1 % 2 * d2_stride; + int head_count = (d1 >> 1) + cnt * (blockDim.y >> 1); + int iteration_stride = blockDim.z * (blockDim.y >> 1); + int matrix_stride = (d0_stride * gridDim.x); + + for (int iter = 0; iter < 2; iter++) { + int iter_row = iter * iteration_stride + head_count; + int iter_offset = (iter_row % blockDim.y) * d2_stride; + + in_data[d3 + iter_offset + (iter_row / blockDim.y + (d1 % 2) * blockDim.z) * d1_stride] = + in_vec[input_offset + iter_offset * seq_length + + (iter_row / blockDim.y) * matrix_stride]; + } + __syncthreads(); + + iteration_stride = d1_stride * blockDim.z; + int iter_index = cnt * d1_stride + d1 * d2_stride + d3; + int output_offset = d0 * d0_stride * blockDim.z + d2 * (iteration_stride << 1); + + for (int iter = 0; iter < 2; iter++) { + int iter_id = iter * iteration_stride + iter_index; + out_vec[output_offset + iter_id] = in_data[iter_id]; + } +#endif +} + +// 3 * [B A S N] - > [B S C*H] +template <> +void launch_transform4d_0213(float* out, + const float* in, + int batch_size, + int heads, + int seq_length, + int hidden_dim, + cudaStream_t stream, + int trans_count) +{ + dim3 grid_dims(batch_size, heads * ((seq_length + 7) / 8), trans_count); + dim3 block_dims(hidden_dim / heads / 4, 8); + transform4d_0213 + <<>>(out, in, heads, seq_length, hidden_dim); +} + +template <> +void launch_transform4d_0213<__half>(__half* out, + const __half* in, + int batch_size, + int heads, + int seq_length, + int hidden_dim, + cudaStream_t stream, + int trans_count) +{ + dim3 grid_dims(batch_size, seq_length / 2); + dim3 block_dims(hidden_dim / heads / 8, heads, trans_count); + transform4d_0213<__half> + <<>>(out, in, heads, seq_length, hidden_dim); +} diff --git a/csrc/type_shim.h b/csrc/type_shim.h deleted file mode 100644 index 80d2c41ec289..000000000000 --- a/csrc/type_shim.h +++ /dev/null @@ -1,131 +0,0 @@ -/* Taken from NVIDIA/apex commit 855808f3fc268e9715d613f3c2e56469d8c986d8 */ -#include - -// Forward/backward compatiblity hack around -// https://github.com/pytorch/pytorch/commit/3aeb78079bcd68282fe9117088e138b77318e288 -// pending more future-proof guidance from upstream. -// struct TypeShim -// { -// const at::Type& payload; -// TypeShim(const at::Type& type) : payload(type) {} -// // Enable trivial conversion to a const at::Type& for pre-3aeb78 -// operator const at::Type&(){ return payload; }; -// // Enable dispatch switch statements to take *this directly for post-3aeb78 -// //operator at::ScalarType(){ return payload.; }; -// }; - -#define DISPATCH_FLOAT_AND_HALF(TYPE, LEVEL, NAME, ...) \ - switch(TYPE) \ - { \ - case at::ScalarType::Float: \ - { \ - using scalar_t_##LEVEL = float; \ - __VA_ARGS__; \ - break; \ - } \ - case at::ScalarType::Half: \ - { \ - using scalar_t_##LEVEL = at::Half; \ - __VA_ARGS__; \ - break; \ - } \ - default: \ - AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ - } - - -#define DISPATCH_DOUBLE_FLOAT_AND_HALF(TYPE, LEVEL, NAME, ...) \ - switch(TYPE) \ - { \ - case at::ScalarType::Double: \ - { \ - using scalar_t_##LEVEL = double; \ - __VA_ARGS__; \ - break; \ - } \ - case at::ScalarType::Float: \ - { \ - using scalar_t_##LEVEL = float; \ - __VA_ARGS__; \ - break; \ - } \ - case at::ScalarType::Half: \ - { \ - using scalar_t_##LEVEL = at::Half; \ - __VA_ARGS__; \ - break; \ - } \ - default: \ - AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ - } - - - #define DISPATCH_DOUBLE_AND_FLOAT(TYPE, LEVEL, NAME, ...) \ - switch(TYPE) \ - { \ - case at::ScalarType::Double: \ - { \ - using scalar_t_##LEVEL = double; \ - __VA_ARGS__; \ - break; \ - } \ - case at::ScalarType::Float: \ - { \ - using scalar_t_##LEVEL = float; \ - __VA_ARGS__; \ - break; \ - } \ - default: \ - AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ - } - - -template -__device__ __forceinline__ T reduce_block_into_lanes - (T *x, - T val, - int lanes=1, - bool share_result=false) // lanes is intended to be <= 32. -{ - int tid = threadIdx.x + threadIdx.y*blockDim.x; - int blockSize = blockDim.x*blockDim.y; // blockSize is intended to be a multiple of 32. - - if(blockSize >= 64) - { - x[tid] = val; - __syncthreads(); - } - - #pragma unroll - for(int i = (blockSize >> 1); i >= 64; i >>= 1) - { - if(tid < i) - x[tid] = x[tid] + x[tid+i]; - __syncthreads(); - } - - T final; - - if(tid < 32) - { - if(blockSize >= 64) - final = x[tid] + x[tid+32]; - else - final = val; - // __SYNCWARP(); - - #pragma unroll - for(int i = 16; i >= lanes; i >>= 1) - final = final + __shfl_down_sync(0xffffffff, final, i); - } - - if(share_result) - { - if(tid < lanes) - x[tid] = final; // EpilogueOp - // Make sure the smem result is visible to all warps. - __syncthreads(); - } - - return final; -} diff --git a/deepspeed/__init__.py b/deepspeed/__init__.py index de482e0c163c..2613aa3f6b93 100755 --- a/deepspeed/__init__.py +++ b/deepspeed/__init__.py @@ -5,6 +5,8 @@ from deepspeed.pt.deepspeed_light import DeepSpeedLight from deepspeed.pt.deepspeed_light import ADAM_OPTIMIZER, LAMB_OPTIMIZER from deepspeed.pt.deepspeed_lr_schedules import add_tuning_arguments +from deepspeed.pt.deepspeed_cuda import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig +from deepspeed.pt.deepspeed_config import DeepSpeedConfig import deepspeed.pt.deepspeed_checkpointing as checkpointing diff --git a/deepspeed/pt/deepspeed_cuda.py b/deepspeed/pt/deepspeed_cuda.py new file mode 100755 index 000000000000..00082d966710 --- /dev/null +++ b/deepspeed/pt/deepspeed_cuda.py @@ -0,0 +1,520 @@ +from torch import nn +from torch.autograd import Function +import torch +import json +import math +import deepspeed_transformer_cuda as ds_transformer_cuda +import deepspeed_stochastic_transformer_cuda as ds_stochastic_transformer_cuda + + +class TransformerConfig(): + def __init__(self, + batch_size, + max_seq_length, + hidden_size, + heads, + attn_dropout_ratio, + hidden_dropout_ratio, + num_hidden_layers, + initializer_range): + self.layer_id = -1 + self.batch_size = batch_size + self.hidden_size = hidden_size + self.max_seq_length = max_seq_length + self.heads = heads + self.attn_dropout_ratio = attn_dropout_ratio + self.hidden_dropout_ratio = hidden_dropout_ratio + self.num_hidden_layers = num_hidden_layers + self.initializer_range = initializer_range + + +class DeepSpeedTransformerConfig(TransformerConfig): + """Initialize the DeepSpeed Transformer Config. + + Arguments: + batch_size: The maximum batch size used for running the kernel on each GPU + + max_seq_length: The sequence-length of the model being trained with DeepSpeed + + hidden_size: The hidden size of the transformer layer + + heads: The number of heads in the self-attention of the transformer layer + + attn_dropout_ratio: The ratio of dropout for the attention's output + + hidden_dropout_ratio: The ratio of dropout for the transformer's output + + num_hidden_layers: The number of transformer layers + + initializer_range: BERT model's initializer range for initializing parameter data + + local_rank: Optional: The rank of GPU running the transformer kernel, it is not required + to use if the model already set the current device, otherwise need to set it + so that the transformer kernel can work on the right device + + seed: The random seed for the dropout layers + + fp16: Enable half-precision computation + + pre_layer_norm: Select between Pre-LN or Post-LN transformer architecture + + normalize_invertible: Optional: Enable invertible LayerNorm execution (dropping the input activation), + default is False + + gelu_checkpoint: Optional: Enable checkpointing of Gelu activation output to save memory, + default is False + + adjust_init_range: Optional: Set as True (default) if the model adjusts the weight initial values of + its self-attention output and layer output, False keeps the initializer_range no change. + See the adjustment below: + output_std = self.config.initializer_range / math.sqrt(2.0 * num_layers) + + attn_dropout_checkpoint: Optional: Enable checkpointing of attention dropout to save memory, + default is False + + stochastic_mode: Enable for high performance, please note that this flag has some level of + non-determinism and can produce different results on different runs. However, we have seen + that by enabling it, the pretraining tasks such as BERT are not affected and can obtain + a high accuracy level. On the other hand, for the downstream tasks, such as fine-tuning, we recommend + to turn it off in order to be able to reproduce the same result through the regular kernel execution. + """ + def __init__(self, + batch_size=-1, + max_seq_length=-1, + hidden_size=-1, + heads=-1, + attn_dropout_ratio=-1, + hidden_dropout_ratio=-1, + num_hidden_layers=-1, + initializer_range=-1, + local_rank=-1, + seed=-1, + fp16=False, + pre_layer_norm=True, + normalize_invertible=False, + gelu_checkpoint=False, + adjust_init_range=True, + attn_dropout_checkpoint=False, + stochastic_mode=False): + super(DeepSpeedTransformerConfig, + self).__init__(batch_size, + max_seq_length, + hidden_size, + heads, + attn_dropout_ratio, + hidden_dropout_ratio, + num_hidden_layers, + initializer_range) + self.fp16 = fp16 + self.pre_layer_norm = pre_layer_norm = True + self.local_rank = local_rank + self.seed = seed + self.normalize_invertible = normalize_invertible + self.gelu_checkpoint = gelu_checkpoint # True: if higher batch size is required + self.adjust_init_range = adjust_init_range + self.test_gemm = False + self.training = True + self.is_grad_enabled = True + self.attn_dropout_checkpoint = attn_dropout_checkpoint + self.stochastic_mode = stochastic_mode + + @classmethod + def from_dict(cls, json_object): + config = DeepSpeedTransformerConfig() + for key, value in json_object.items(): + config.__dict__[key] = value + return config + + @classmethod + def from_json_file(cls, json_file): + with open(json_file, "r", encoding='utf-8') as reader: + text = reader.read() + return cls.from_dict(json.loads(text)) + + +class DeepSpeedTransformerFunction(Function): + @staticmethod + def forward(ctx, + input, + input_mask, + self, + grads, + layer_id, + attn_qkvw, + attn_qkvb, + attn_ow, + attn_ob, + attn_nw, + attn_nb, + inter_w, + inter_b, + output_w, + output_b, + norm_w, + norm_b, + config): + + bsz = input.shape[0] + + if bsz > config.batch_size: + raise ValueError('Input batch size exceeds the limit.') + + cuda_module = ds_stochastic_transformer_cuda if config.stochastic_mode else ds_transformer_cuda + forward_func = cuda_module.forward_fp16 if config.fp16 else cuda_module.forward_fp32 + + (output, + inp_norm, + qkv_tf, + soft_inp, + ctx_bufB, + attn_o_inp, + add_res, + ff1_inp, + gelu_inp, + ff2_inp, + attn_prob_dropout_mask, + attn_output_dropout_mask, + layer_output_dropout_mask) = forward_func(config.layer_id, + input, + input_mask, + attn_qkvw, + attn_qkvb, + attn_ow, + attn_ob, + attn_nw, + attn_nb, + inter_w, + inter_b, + output_w, + output_b, + norm_w, + norm_b, + config.training, + config.pre_layer_norm, + config.attn_dropout_checkpoint, + config.normalize_invertible, + config.gelu_checkpoint) + + # For testing only. + if grads is not None: + for i in [2]: + attn_qkvw.register_hook( + lambda x, + i=i, + self=self: grads.append([ + x[i * attn_ow.size(0):(i + 1) * attn_ow.size(0)], + ("Q_W" if i == 0 else "K_W" if i == 1 else "V_W") + ])) + for i in [2]: + attn_qkvb.register_hook( + lambda x, + i=i, + self=self: grads.append([ + x[i * attn_ow.size(0):(i + 1) * attn_ow.size(0)], + ("Q_B" if i == 0 else "K_B" if i == 1 else "V_B") + ])) + + attn_ow.register_hook(lambda x, self=self: grads.append([x, "O_W"])) + attn_ob.register_hook(lambda x, self=self: grads.append([x, "O_B"])) + attn_nw.register_hook(lambda x, self=self: grads.append([x, "N2_W"])) + attn_nb.register_hook(lambda x, self=self: grads.append([x, "N2_B"])) + inter_w.register_hook(lambda x, self=self: grads.append([x, "int_W"])) + inter_b.register_hook(lambda x, self=self: grads.append([x, "int_B"])) + output_w.register_hook(lambda x, self=self: grads.append([x, "out_W"])) + output_b.register_hook(lambda x, self=self: grads.append([x, "out_B"])) + norm_w.register_hook(lambda x, self=self: grads.append([x, "norm_W"])) + norm_b.register_hook(lambda x, self=self: grads.append([x, "norm_B"])) + + if config.is_grad_enabled: + if (config.pre_layer_norm and config.normalize_invertible): + ctx.save_for_backward(input_mask, + attn_qkvw, + attn_qkvb, + attn_ow, + attn_ob, + attn_nw, + attn_nb, + inter_w, + inter_b, + output_w, + output_b, + norm_w, + norm_b) + else: + ctx.save_for_backward(output, + input, + input_mask, + attn_qkvw, + attn_qkvb, + attn_ow, + attn_ob, + attn_nw, + attn_nb, + inter_w, + inter_b, + output_w, + output_b, + norm_w, + norm_b) + + ctx.config = config + if (config.pre_layer_norm or not config.normalize_invertible): + ctx.inp_norm = inp_norm + + ctx.qkv_tf = qkv_tf + ctx.soft_inp = soft_inp + if not config.attn_dropout_checkpoint: + ctx.ctx_bufB = ctx_bufB + + ctx.attn_o_inp = attn_o_inp + if not config.normalize_invertible: + ctx.add_res = add_res + + ctx.ff1_inp = ff1_inp + if not config.gelu_checkpoint: + ctx.gelu_inp = gelu_inp + + ctx.ff2_inp = ff2_inp + ctx.attn_prob_dropout_mask = attn_prob_dropout_mask + ctx.attn_output_dropout_mask = attn_output_dropout_mask + ctx.layer_output_dropout_mask = layer_output_dropout_mask + + return output + + @staticmethod + def backward(ctx, grad_output): + bsz = grad_output.shape[0] + + if bsz > ctx.config.batch_size: + raise ValueError('grad_output batch size exceeds the limit.') + + assert ctx.config.training + + if (ctx.config.pre_layer_norm and ctx.config.normalize_invertible): + (input_mask, + attn_qkvw, + attn_qkvb, + attn_ow, + attn_ob, + attn_nw, + attn_nb, + inter_w, + inter_b, + output_w, + output_b, + norm_w, + norm_b) = ctx.saved_tensors + else: + (output, + input, + input_mask, + attn_qkvw, + attn_qkvb, + attn_ow, + attn_ob, + attn_nw, + attn_nb, + inter_w, + inter_b, + output_w, + output_b, + norm_w, + norm_b) = ctx.saved_tensors + + cuda_module = ds_stochastic_transformer_cuda if ctx.config.stochastic_mode else ds_transformer_cuda + backward_func = cuda_module.backward_fp16 if ctx.config.fp16 else cuda_module.backward_fp32 + + (grad_input, + grad_attn_qkvw, + grad_attn_qkvb, + grad_attn_ow, + grad_attn_ob, + grad_attn_nw, + grad_attn_nb, + grad_inter_w, + grad_inter_b, + grad_output_w, + grad_output_b, + grad_norm_w, + grad_norm_b) = backward_func( + ctx.config.layer_id, + grad_output, + (ctx.inp_norm if (ctx.config.pre_layer_norm + and ctx.config.normalize_invertible) else output), + (ctx.inp_norm if (ctx.config.pre_layer_norm + or not ctx.config.normalize_invertible) else input), + ctx.qkv_tf, + ctx.soft_inp, + (ctx.soft_inp if ctx.config.attn_dropout_checkpoint else ctx.ctx_bufB), + ctx.attn_o_inp, + (ctx.ff1_inp if ctx.config.normalize_invertible else ctx.add_res), + ctx.ff1_inp, + (ctx.ff2_inp if ctx.config.gelu_checkpoint else ctx.gelu_inp), + ctx.ff2_inp, + ctx.attn_prob_dropout_mask, + ctx.attn_output_dropout_mask, + ctx.layer_output_dropout_mask, + (ctx.inp_norm if (ctx.config.pre_layer_norm + and ctx.config.normalize_invertible) else input), + input_mask, + attn_qkvw, + attn_qkvb, + attn_ow, + attn_ob, + attn_nw, + attn_nb, + inter_w, + inter_b, + output_w, + output_b, + norm_w, + norm_b) + + return (grad_input, + None, + None, + None, + None, + grad_attn_qkvw, + grad_attn_qkvb, + grad_attn_ow, + grad_attn_ob, + grad_attn_nw, + grad_attn_nb, + grad_inter_w, + grad_inter_b, + grad_output_w, + grad_output_b, + grad_norm_w, + grad_norm_b, + None) + + +class DeepSpeedTransformerLayer(nn.Module): + """Initialize the DeepSpeed Transformer Layer. + + Arguments: + layer_id: The layer index starting from 0, e.g. if model has 24 transformer layers, + layer_id will be 0,1,2...23 when each layer object is instantiated + + config: An object of DeepSpeedTransformerConfig + + initial_weights: Optional: Only used for unit test + + initial_biases: Optional: Only used for unit test + """ + def __init__(self, layer_id, config, initial_weights=None, initial_biases=None): + super(DeepSpeedTransformerLayer, self).__init__() + + self.config = config + self.config.layer_id = layer_id + + print("DeepSpeed Transformer config is ", self.config.__dict__) + + if self.config.local_rank >= 0: + torch.cuda.set_device(self.config.local_rank) + + if initial_weights is None and initial_biases is None: + self.attn_qkvw = nn.Parameter( + torch.Tensor(self.config.hidden_size * 3, + self.config.hidden_size)) + self.attn_qkvb = nn.Parameter(torch.Tensor(self.config.hidden_size * 3)) + self.attn_ow = nn.Parameter( + torch.Tensor(self.config.hidden_size, + self.config.hidden_size)) + self.attn_ob = nn.Parameter(torch.Tensor(self.config.hidden_size)) + self.attn_nw = nn.Parameter(torch.Tensor(self.config.hidden_size)) + self.attn_nb = nn.Parameter(torch.Tensor(self.config.hidden_size)) + self.inter_w = nn.Parameter( + torch.Tensor(4 * self.config.hidden_size, + self.config.hidden_size)) + self.inter_b = nn.Parameter(torch.Tensor(4 * self.config.hidden_size)) + self.output_w = nn.Parameter( + torch.Tensor(self.config.hidden_size, + 4 * self.config.hidden_size)) + self.output_b = nn.Parameter(torch.Tensor(self.config.hidden_size)) + self.norm_w = nn.Parameter(torch.Tensor(self.config.hidden_size)) + self.norm_b = nn.Parameter(torch.Tensor(self.config.hidden_size)) + self.init_transformer_weights(self.config.adjust_init_range) + else: + # For testing only. + self.attn_qkvw = nn.Parameter( + torch.Tensor(self.config.hidden_size * 3, + self.config.hidden_size)) + for i in range(3): + self.attn_qkvw[i * self.config.hidden_size:(i + 1) * self.config.hidden_size] = \ + torch.empty_like(initial_weights[i]).copy_(initial_weights[i]) + self.attn_qkvb = nn.Parameter(torch.Tensor(self.config.hidden_size * 3)) + self.attn_qkvb.data.zero_() + self.attn_ow = initial_weights[3] + self.attn_ob = initial_biases[3] + self.attn_nw = initial_weights[4] + self.attn_nb = initial_biases[4] + self.inter_w = initial_weights[5] + self.inter_b = initial_biases[5] + self.output_w = initial_weights[6] + self.output_b = initial_biases[6] + self.norm_w = initial_weights[7] + self.norm_b = initial_biases[7] + + # create the layer in cuda kernels. + cuda_module = ds_stochastic_transformer_cuda if self.config.stochastic_mode else ds_transformer_cuda + create_layer_func = cuda_module.create_transformer_layer_fp16 if self.config.fp16 else cuda_module.create_transformer_layer_fp32 + + create_layer_func(self.config.layer_id, + self.config.batch_size, + self.config.hidden_size, + self.config.heads, + 4 * self.config.hidden_size, + self.config.max_seq_length, + self.config.attn_dropout_ratio, + self.config.hidden_dropout_ratio, + self.config.seed, + self.config.pre_layer_norm, + self.config.test_gemm, + self.config.attn_dropout_checkpoint, + self.config.normalize_invertible, + self.config.gelu_checkpoint, + self.config.stochastic_mode) + + def init_transformer_weights(self, adjust_init_range=False): + num_layers = self.config.num_hidden_layers + output_std = self.config.initializer_range + if adjust_init_range and self.config.local_rank == 0: + print("Accounting for accumulation on the residual path") + output_std = self.config.initializer_range / math.sqrt(2.0 * num_layers) + + self.attn_qkvw.data.normal_(mean=0.0, std=self.config.initializer_range) + self.attn_qkvb.data.zero_() + self.attn_ow.data.normal_(mean=0.0, std=output_std) + self.attn_ob.data.zero_() + self.attn_nw.data.fill_(1.0) + self.attn_nb.data.zero_() + self.inter_w.data.normal_(mean=0.0, std=self.config.initializer_range) + self.inter_b.data.zero_() + self.output_w.data.normal_(mean=0.0, std=output_std) + self.output_b.data.zero_() + self.norm_w.data.fill_(1.0) + self.norm_b.data.zero_() + + def forward(self, input, input_mask, grads=None): + self.config.training = self.training + self.config.is_grad_enabled = torch.is_grad_enabled() + return DeepSpeedTransformerFunction.apply(input, + input_mask, + self, + grads, + self.config.layer_id, + self.attn_qkvw, + self.attn_qkvb, + self.attn_ow, + self.attn_ob, + self.attn_nw, + self.attn_nb, + self.inter_w, + self.inter_b, + self.output_w, + self.output_b, + self.norm_w, + self.norm_b, + self.config) diff --git a/deepspeed/pt/deepspeed_fused_lamb.py b/deepspeed/pt/deepspeed_fused_lamb.py index 9aad43197f6b..387421ffe9b8 100644 --- a/deepspeed/pt/deepspeed_fused_lamb.py +++ b/deepspeed/pt/deepspeed_fused_lamb.py @@ -58,7 +58,7 @@ def __init__(self, min_coeff=0.01, amsgrad=False): global fused_lamb_cuda - fused_lamb_cuda = importlib.import_module("fused_lamb_cuda") + fused_lamb_cuda = importlib.import_module("deepspeed_lamb_cuda") if amsgrad: raise RuntimeError('FusedLamb does not support the AMSGrad variant.') diff --git a/deepspeed/pt/deepspeed_run.py b/deepspeed/pt/deepspeed_run.py index f4ceca4220ed..f9f032066ebd 100755 --- a/deepspeed/pt/deepspeed_run.py +++ b/deepspeed/pt/deepspeed_run.py @@ -283,7 +283,9 @@ def main(args=None): "-u", "-m", "deepspeed.pt.deepspeed_launch", - "--world_info={}".format(world_info_base64) + "--world_info={}".format(world_info_base64), + "--master_addr={}".format(args.master_addr), + "--master_port={}".format(args.master_port) ] cmd = deepspeed_launch + [args.user_script] + args.user_args else: diff --git a/docs/_posts/2020-05-19-bert-record.md b/docs/_posts/2020-05-19-bert-record.md index 2f41cfbbdfac..d8cdac6a5d7d 100644 --- a/docs/_posts/2020-05-19-bert-record.md +++ b/docs/_posts/2020-05-19-bert-record.md @@ -17,6 +17,7 @@ DeepSpeed achieves the fastest BERT training record: 44 minutes on 1,024 NVIDIA V100 GPUs**, compared with the best published result of 67 minutes on the same number and generation of GPUs. -For a technical overview, see our [blog post](https://www.microsoft.com/en-us/research/blog/zero-2-deepspeed-shattering-barriers-of-deep-learning-speed-scale/). - -**Code and tutorials are coming soon!** +* Brief overview, see our [press release](https://www.microsoft.com/en-us/research/blog/zero-2-deepspeed-shattering-barriers-of-deep-learning-speed-scale/). +* Detailed technology deep dive, see our [blog post](https://www.deepspeed.ai/news/2020/05/28/bert-record.html). +* Tutorial on how to reproduce our results, see our [BERT pre-training tutorial](https://www.deepspeed.ai/tutorials/bert-pretraining/). +* The source code for our transformer kernels can be found in the [DeepSpeed repo](https://github.com/microsoft/deepspeed) and BERT pre-training code can be found in the [DeepSpeedExamples repo](https://github.com/microsoft/deepspeedexamples). diff --git a/docs/_posts/2020-05-28-fastest-bert-training.md b/docs/_posts/2020-05-28-fastest-bert-training.md new file mode 100644 index 000000000000..9b3e1fcbc859 --- /dev/null +++ b/docs/_posts/2020-05-28-fastest-bert-training.md @@ -0,0 +1,268 @@ +--- +layout: single +title: "Microsoft DeepSpeed achieves the fastest BERT training time" +excerpt: "" +categories: news +new_post: true +date: 2020-05-28 00:00:00 +--- + +Good news! **DeepSpeed obtains the fastest BERT training record: 44 minutes on +1024 NVIDIA V100 GPU.** This is a 30% improvement over the best published result +of 67 mins in end-to-end training time to achieve the same accuracy on the same +number and generation of GPUs. This improvement does not come at the cost of +excessive hardware resources but comes from improved software efficiency. For +example, DeepSpeed can attain a staggering 64 teraflops of single GPU +performance on a NVIDIA V100 GPU which is over 50% of the hardware peak. + +In this blog post, we will discuss four technological improvements that enable +DeepSpeed to achieve this record-breaking BERT training time. + +1. Highly optimized transformer kernels to improve compute efficiency +2. Overlapping I/O with computation through asynchronous prefetching queue +3. Sparse output processing to eliminate wasteful computation +4. Layer-norm reordering for training stability and faster convergence + +These optimizations not only benefit BERT; they are also applicable to many +other transformer-based models such as RoBERTa, XLNet, and UniLM. + +## Overview of Performance Results + +Compared to SOTA, DeepSpeed significantly improves single GPU performance for +transformer-based model like BERT. Figure 1 shows the single GPU throughput of +training BERT-Large optimized through DeepSpeed, comparing with the two +well-known PyTorch implementations from [NVIDIA +BERT](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT) +and [Hugging Face +BERT](https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_bert.py). +DeepSpeed reaches as high as 64 and 53 teraflops throughputs (corresponding to +272 and 52 samples/second) for sequence lengths 128 and 512, respectively, +exhibiting up to 28% throughput improvements over NVIDIA BERT and up to 62% +over HuggingFace BERT. We also support up to 1.8x larger batch size without +running out of memory. + +To achieve this performance, DeepSpeed implements a stochastic transformer +which exhibits some level of non-deterministic noise without affecting overall +convergence. In addition, DeepSpeed also implements a deterministic transformer +kernel that is completely reproducible at the expense of a small performance +regression of approximately 2% on average. Users can easily choose and switch +between the two versions depending on their usage scenarios: Stochastic version +pursues ultimate training performance goal, and deterministic version may save +development time by better facilitating experimentation and debugging. We +report performance numbers for both these kernels in Figure 1. The performance +numbers were collected with a gradient accumulation step of 10 for all batch +sizes and configurations, since on average an overall batch size used in +practical scenarios range from a few hundred to a few thousand. + + +![Transformer-Kernel-Throughput-128](../assets/images/transformer_kernel_perf_seq128.PNG) + +![Transformer-Kernel-Throughput-512](../assets/images/transformer_kernel_perf_seq512.PNG) + +Figure 1: Performance evaluation of BERT-Large on a single V100 GPU, comparing +DeepSpeed with NVIDIA and HuggingFace versions of BERT in mixed-sequence length +training. The labeled points show the highest throughput of each implementation +in teraflops (Tflops). DeepSpeed boosts throughput and allows for higher batch +sizes without running out-of-memory. + +Looking at distributed training across GPUs, Table 1 shows our end-to-end +BERT-Large pretraining time (F1 score of 90.5 for SQUAD) using 16 to 1024 GPUs. +We complete BERT pretraining in 44 minutes using 1024 V100 GPUs (64 NVIDIA +DGX-2 nodes). In comparison, the previous SOTA from NVIDIA takes 47 mins using +1472 V100 GPUs. DeepSpeed is not only faster but also uses 30% less resources. +Using the same 1024 GPUS,NVIDIA BERT takes 67 minutes using the same 1024 GPUs +[1] BERT, whereas DeepSpeed takes 44 minutes, reducing training time by 30%. +Similarly, on 256 GPUs, NVIDIA BERT takes 236 minutes while DeepSpeed takes 144 +minutes (39% faster). + +| Number of nodes | Number of V100 GPUs | Time | +| ----------------- | -------------------- | ------------ | +| 1 DGX-2 | 16 | 33 hr 13 min | +| 4 DGX-2 | 64 | 8 hr 41 min | +| 16 DGX-2 | 256 | 144 min | +| 64 DGX-2 | 1024 | 44 min | + +Table 1: BERT-Large training time using 1 to 64 DGX-2's with DeepSpeed. + +At the recent GTC 2020, NVIDIA announced the next generation hardware A100, +which now offers 2.5X hardware peak performance over the V100 GPU. Assuming +the A100 GPU allows us to obtain the same percentage of hardware peak +performance (50%) as we obtained on V100 GPUs, we expect to obtain even higher +throughput by combining our software optimizations with the new hardware. We +project it would reduce BERT training time further to less than 25 minutes on a +cluster of 1024 A100 GPUs. + +## BERT Highly Optimized Transformer Kernels + +GPUs have very high peak floating-point throughput, but the default Transformer +blocks in most framework implementations are far from reaching this peak. +Figure 2 shows the structure of a Transformer block with the LayerNorm placed +on the input stream of the two sublayers: Attention and Feed-Forward. To +approach the GPU peak performance, we employ two lines of optimizations in our +own Transformer kernel implementation: advanced fusion, and invertible +operators. + +![Transformer-PreLN-Arch](../assets/images/transformer_preln_arch.png) + +Figure 2: Transformer Layer with Pre-LayerNorm Architecture + +### (a) Advanced fused kernels to reduce data movement + +We observe that transformer-based networks trigger many invocations of CUDA +kernels operating in a producer-consumer fashion, adding a lot of cost for +transferring data to and from global memory and overhead from kernel launching. +Existing compiler-based approaches perform fine-grained fusion (e.g., fusion of +element-wise operations), leading to missed fusion opportunities. In contrast, +we fully exploit both fine-grain and coarse-grained fusion, tailored for +Transformer blocks. + +**QKV and various fusions.** We merge the three Query (Q), Key (K), and Value (V) +weight matrices to dispatch a larger QKV GEMM to expose more parallelism and +improve data locality on GPU’s shared memory and register files, as shown in +Figure 3. Next, we combine the data-layout transformation of the QKV’s output +matrix with the bias addition. We then partition the large QKV matrix into +three transformed ones, used for the following self-attention computation. + +As Figure 3 illustrates, we read the QKV matrix in consecutive rows (shown by +red box), and write them in the three transformed Q, K, and V matrices. Since +each matrix starts from a different offset, we may have uncoalesced access to +the main memory. Thus, we use the shared memory as an intermediate buffer, in +order to rearrange the data in a way that we can put the data in consecutive +parts of memory. Even though we produce an uncoalesced pattern when accessing +shared memory, we reduce the cost of uncoalesced access to main memory to +better exploit memory bandwidth, resulting in 3% to 5% performance improvement +in the end-to-end training. + +![QKV-Fusion](../assets/images/qkv_fusion.png) + +Figure 3: QKV’s GEMM and transform Kernel-Fusion + +We perform additional fusions such as merging the addition of bias from the +attention-output GEMM with the addition from the residual connection and also +dropout, which allows accesses to happen in the register files and shared +memory, which are orders of magnitude faster than the expensive write-back to +the global memory. + +**Warp-level communication.** To alleviate the synchronization overhead among +parallel GPU cores and further increase the resource utilization of the fused +kernels, we use the warp-level (data shuffle instructions) instead of the +default inter-warp communication. Take the layer-normalization and SoftMax +kernel as examples, we perform each reduction operation inside a warp, while +distributing different reductions across different warps. This way, we +alleviate the synchronization among the parallel threads and further increase +the GPU resource utilization. + +**Stochastic vs deterministic kernels.** DL training is generally robust to some +level of stochasticity, and in some cases, controlled noises such as dropouts +act as regularizer which improve generalization. In designing our transformer +kernel, we embrace some level of stochasticity to improve throughput by +allowing for limited data race conditions to exist in the kernel: We leverage +implicit warp synchronous programming to achieve higher performance for the +warp-level cooperative operations [3]. The lack of explicit warp level +synchronization act as non-deterministic noise without affecting the overall +convergence behavior of the transformer kernels while giving a decent +throughput boost. + +In addition, DeepSpeed also implements a non-stochastic transformer kernel with +explicit warp synchronization that produces deterministic results at the +expense of a small performance regression. Users can easily choose and switch +between the two versions depending on their usage scenarios: Stochastic version +pursues ultimate training performance goal, and deterministic version may save +development time by better facilitating experimentation and debugging. + +In our experiments, we use stochastic kernels for the pretraining BERT, while +using non-stochastic kernels for fine-tuning to achieve fully reproducible +results. We recommend using stochastic kernels for training tasks involving +massive amounts of data such as pre-training, while using non-stochastic +version when training with limited data such as in the case of fine-tuning for +more consistent results. + +**Cost-effective rematerialization.** When fusing kernels of the different +operations, we observe that some operators are inexpressive to compute but +incur expensive data movement cost, such as addition of bias and dropout. For +these operations, we avoid saving their results in the forward pass, but +instead recompute them during the backward pass, which turns out to be much +faster than having their results written and reloaded from the main memory. + +### (b) Invertible operators to save memory and run large batches + +We also observe that the intermediate activations from several operators in the +Transformer blocks incur a large memory consumption, such as SoftMax and Layer +Norm. For these operators, we drop the inputs to these layers to reduce the +footprint of activation memory, by leveraging the fact that they are invertible +functions, which are functions whose backward pass is independent of the inputs +and can be formulated based only on the outputs [2]. Figure 4 and Figure 5 show +the examples of the original implementation of SoftMax and Layer-Norm in +PyTorch versus the invertible SoftMax implementation in DeepSpeed. Through this +optimization, we are able to reduce the activation memory of the operator by +half, and the reduced memory allows us to train with larger batch sizes, which +once again improves GPU efficiency. + +![Softmax-torch](../assets/images/softmax_pytorch.gif) + +![Softmax-DS](../assets/images/softmax_deepspeed.gif) + +Figure 4: DeepSpeed invertible SoftMax operation versus Default PyTorch SoftMax operation + +![LayerNorm-DS](../assets/images/layernorm_pytorch.gif) + +![LayerNorm-DS](../assets/images/layernorm_deepspeed.gif) + +Figure 5: DeepSpeed invertible LayerNorm operation versus Default PyTorch LayerNorm operation + +## Overlapping I/O with Computation through Asynchronous Prefetching Queue + +Beyond highly optimized transformer kernels, the BERT training has other +performance limiting factors, e.g., data loading. We develop our own +asynchronous worker which prefetches batches of data into a queue only at “safe +points” -- points when the CPUs are idle (e.g., right after asynchronously +launching the forward pass). In this way, we make sure that there is no +dequeuing and copying data from CPU to GPU when there is computation on the CPU +side. This is different from the default PyTorch data loader, which can +prefetch data at any points and cause performance interference. By using this +method, we hide almost all I/O overhead, which accounts for 4% of the original +training time. + +## Exploiting Sparsity of BERT’s Output Processing + +We improve the end-to-end training time by 5.4% by recognizing and exploiting +sparsity in BERT’s output processing. The output processing involves two steps: +i) BERT projection from the hidden output dimension of the final transformer +layer to the language vocabulary, using a matrix-matrix multiplication, and ii) +a cross-entropy of the masked output tokens to the get each sequence’s +prediction error. The cost of the first step is proportional to the vocabulary +size, hidden output dimension and the sequence length, and can be as expensive +as a transformer layer computation or more. However, only about 15% of the +tokens are masked, and we only need the cross-entropy for the masked tokens. +Therefore, the projection can be done as an efficient sparse computation. To do +so, we discard the rows of the final transformer layer that corresponding to +the non-masked tokens before doing the projection, reducing the computation +cost of output processing by 85%. + +## Pre-LayerNorm vs Post-LayerNorm Architecture + +We observe that with large batch size (e.g., 64K) the default BERT pre-training +suffers from training instability, which can result in model divergence or +convergence to bad/suspicious local optima. Further investigation shows that +the default BERT has vanishing gradients issue. To mitigate the issue, we +changed the placement of LayerNorm (Post-LayerNorm) by placing it only on the +input stream of the sublayers in the Transformer block (called Pre-LayerNorm), +a modification described by several recent works for neural machine +translation. The Pre-LayerNorm results in several useful characteristics such +as avoiding vanishing gradient, stable optimization, and performance gain. It +allows us to train at aggregated batch size of 64K with increased learning rate +and faster convergence. + + +To try out these optimizations and training recipe, please check out our [BERT +training tutorial](https://www.deepspeed.ai/tutorials/bert-pretraining/) and +source code at the [DeepSpeed GitHub +repo](https://github.com/microsoft/deepspeed). + +### References + +[1] "NVIDIA Clocks World’s Fastest BERT Training Time and Largest Transformer Based Model, Paving Path For Advanced Conversational AI" [https://devblogs.nvidia.com/training-bert-with-gpus/](https://devblogs.nvidia.com/training-bert-with-gpus/). + +[2] S. R. Bulo, L. Porzi, and P. Kontschieder, "In-place activated batch norm for memory-optimized training of dnns" 2017. [http://arxiv.org/abs/1712.02616](http://arxiv.org/abs/1712.02616). + +[3] Mark Harris and Kyrylo Perelygin, "Cooperative Groups: Flexible CUDA Thread Programming", [https://devblogs.nvidia.com/cooperative-groups/]( https://devblogs.nvidia.com/cooperative-groups/). diff --git a/docs/_tutorials/bert-finetuning.md b/docs/_tutorials/bert-finetuning.md new file mode 100755 index 000000000000..181ef43b4825 --- /dev/null +++ b/docs/_tutorials/bert-finetuning.md @@ -0,0 +1,189 @@ +--- +title: "BingBertSQuAD Fine-tuning" +excerpt: "" +--- + +In this tutorial we will be adding DeepSpeed to the BingBert model for the SQuAD fine-tuning task, called "BingBertSquad" henceforth. We will also demonstrate performance gains. + +## Overview + +Please clone the repository and go to the `examples/BingBertSquad` folder to follow along. + +### Pre-requisites + +* Download SQuAD data: + * Training set: [train-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json) + * Validation set: [dev-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json) + +You also need a pre-trained BERT model checkpoint from DeepSpeed. We will use checkpoint 162 from the +BERT pre-training [tutorial](bert-pretraining). + + * Pre-training checkpoint: `training_state_checkpoint_162.tar` + +Note that the BERT model in the file `train-v1.1.json_bert-large-uncased_384_128_64` is not strictly required as it will be downloaded automatically if it is not present locally on the cluster. + +### Running BingBertSquad + +- **Unmodified (BaseLine):** If you would like to run unmodified BingBertSquad with the pre-processed data, there is a helper script which you can invoke via: `bash run_squad_baseline.sh 8 /training_state_checkpoint_162.tar` where the first argument `8` is the number of GPUs and the second argument is the path to the pre-training checkpoint. This bash script sets the parameters and invokes `nvidia_run_squad_baseline.py`. +- **Modified (DeepSpeed):** This is similar to baseline; just substitute `run_squad_baseline.sh` with `run_squad_deepspeed.sh` which invokes `nvidia_run_squad_deepspeed.py`. + +## DeepSpeed Integration + +The main DeepSpeed modified script is `nvidia_run_squad_deepspeed.py`; the line `import deepspeed` enables you to use DeepSpeed. + +Make sure that the number of GPUs specified in the job are available (else, this will yield an out of memory error). The wrapper script `run_BingBertSquad.sh` and the test script `run_tests.sh` essentially serve to automate training - they may also be used a guidelines to set parameters and launch the fine-tuning task. + +### Configuration + +The `deepspeed_bsz24_config.json` file gives the user to specify DeepSpeed options in terms of batch size, learning rate, precision and other parameters. When running the `nvidia_run_squad_deepspeed.py`, in addition to the `-d` flag to enable DeepSpeed, the appropriate DeepSpeed configuration file must be specified using `--deepspeed_config `. + +### Argument Parsing + +The first step to apply DeepSpeed is adding arguments to BingBertSquad, using `deepspeed.add_config_arguments()` in the beginning of the main entry point as in the `main()` function in `nvidia_run_squad_deepspeed.py`. The argument passed to `add_config_arguments()` is obtained from the `get_argument_parser()` function in utils.py. + +```python +parser = get_argument_parser() +# Include DeepSpeed configuration arguments +parser = deepspeed.add_config_arguments(parser) +args = parser.parse_args() +``` + +Similar to this, all the options with their corresponding description are available in `utils.py`. + + +### Training + +#### Initialization + +DeepSpeed has an initialization function to create model, optimizer and LR scheduler. For BingBertSquad, we simply augment the Baseline script with the initialize function as follows. + +```python +model, optimizer, _, _ = deepspeed.initialize(args=args, + model=model, + model_parameters=optimizer_grouped_parameters, + dist_init_required=False) + +``` + +Another feature of DeepSpeed is its convenient `step()` function which can be called directly as `model.step()` which hides the `fp16_optimizer` away from the user as opposed to `optimizer.step()` in the baseline code (similar to other models in this tutorial) which needs explicit handling of the case of FP16 computation. + +#### Forward pass + +This is identical in both Baseline and DeepSpeed, and is performed by `loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions)`. + +#### Backward pass + +In the Baseline script you need to handle the all-reduce operation at the gradient accumulation boundary explicitly by using `enable_need_reduction()` followed by `optimizer.backward(loss)` in FP16 and `loss.backward()` in FP32. In DeepSpeed, you may simply do `model.backward(loss)`. + +#### Weight updates + +In the Baseline Script, you are required to explicitly specify the optimizer as `FusedAdam` (along with the handling of dynamic loss scaling) in FP16 and `BertAdam` in FP32, followed by the call `optimizer.step()` and `optimizer.zero_grad()`. DeepSpeed handles this internally (by setting the optimizer using the JSON config) when `initialize()` is called and thus you don't need to explicitly write code but just do `model.step()`. + +Congratulations! Porting into DeepSpeed is complete. + +### Evaluation + +Once training is complete, the EM and F1 scores may be obtained from the following command: + +`python evaluate-v1.1.py /dev-v1.1.json /predictions.json` + +### DeepSpeed Improvements + +The table summarizing the results are given below. In all cases, the batch size is set to 24 and the training is conducted on 8 GPUs for 2 epochs on the DLTS RR1 DGX-2 hypercluster. A set of parameters (seeds and learning rates) were tried and the best ones were selected. All learning rates was 3e-5; Baseline seeds were 42 and DeepSpeed seeds were 10. + +| Case | Precision | EM | F1 | Throughput | +| --------- | --------- | ----- | ----- | ---------- | +| DeepSpeed | FP16 | 84.38 | 91.11 | 9.6 | +| Baseline | FP16 | 84.39 | 91.29 | 8.4 | +| DeepSpeed | FP32 | 84.20 | 91.06 | 3.7 | +| Baseline | FP32 | 84.20 | 90.91 | 2.7 | + +In terms of throughput (expressed in iterations processed per second), we note that DeepSpeed outperforms the baseline for the desired accuracy (in terms of EM, F1 scores). + +## Fine-tuning the model pre-trained with DeepSpeed Transformer Kernels +For pre-training your model, please see [BERT Pre-Training](\bert-pretraining\) tutorial for the detailed instrucions. +If you already obtained the checkpoint of your model, use the following configuration to finetune your pretrained checkpoint. + +| Parameters | Value | +| ------------------------ | ------------------------- | +| Total batch size | 24 | +| Train micro batch size per gpu | 3 | +| Optimizer | Adam | +| Learning rate | 4e-5 | +| Sequence-length | 384 | +| Weight-decay | 0.0 | +| Epoch count | 2 | + +### Enabling DeepSpeed's Transformer Kernel + +DeepSpeed's optimized transformer kernel must be enabled during fine-tuning +if and only if it was used also during pre-training, because the transformer +kernel has its own parameters saved in checkpoint files. + +To enable the transformer kernel for higher performance, first add an argument +`--deepspeed_transformer_kernel` in `utils.py`, we can set it as `False` by +default, for easily turning on/off. + +```python + parser.add_argument('--deepspeed_transformer_kernel', + default=False, + action='store_true', + help='Use DeepSpeed transformer kernel to accelerate.') +``` + +Then in the `BertEncoder` class of the modeling source file, instantiate +transformer layers using the DeepSpeed transformer kernel as below. + +```python + if args.deepspeed_transformer_kernel: + from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig, DeepSpeedConfig + + if hasattr(args, 'deepspeed_config') and args.deepspeed_config: + ds_config = DeepSpeedConfig(args.deepspeed_config) + else: + raise RuntimeError('deepspeed_config is not found in args.') + + cuda_config = DeepSpeedTransformerConfig( + batch_size = ds_config.train_micro_batch_size_per_gpu, + max_seq_length = args.max_seq_length, + hidden_size = config.hidden_size, + heads = config.num_attention_heads, + attn_dropout_ratio = config.attention_probs_dropout_prob, + hidden_dropout_ratio = config.hidden_dropout_prob, + num_hidden_layers = config.num_hidden_layers, + initializer_range = config.initializer_range, + seed = args.seed, + fp16 = ds_config.fp16_enabled, + pre_layer_norm=True) + + self.layer = nn.ModuleList([copy.deepcopy(DeepSpeedTransformerLayer(i, cuda_config)) for i in range(config.num_hidden_layers)]) + else: + layer = BertLayer(config) + self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]) + +``` + +All configuration settings come from the DeepSpeed configuration file and +command arguments and thus we must pass the `args` variable to here in this model. + +Note: + +1. `batch_size` is the maximum bath size of input data, all fine-tuning training data or prediction data shouldn't exceed this threshold, otherwise it will throw an exception. In the DeepSpeed configuration file micro batch size is defined as `train_micro_batch_size_per_gpu`, e.g. if it is set as 8 and prediction uses batch size of 12, we can use 12 as transformer kernel batch size, or using "--predict_batch_size" argument to set prediction batch size to 8 or a smaller number. +2. `local_rank` in DeepSpeedTransformerConfig is used to assign the transformer kernel to the correct device. Since the model already runs set_device() before here, so does not need to be set here. + +For more details about the transformer kernel, please see [DeepSpeed Transformer Kernel](/transformer_kernel/) and [DeepSpeed Fast-Bert Training](/fast_bert/). + +### Dropout Setting +For the fine-tuning, we only use the deterministic transformer to have reproducible the fine-tuning results. But, we choose different values for dropout based on whether pre-training was done using deterministic or stochastic transformer (Please see [Transformer tutorial](/transformer_kernel/) for more detail of selecting these two modes). + +For model pre-trained with deterministic transformer, we use the same dropout ration used in pretraining (0.1). However, we slightly increase the dropout ratio when fine-tuning the model pre-trained using the stochastic transformer to compensate for the lack of stochastic noise during fune-tuning. + + +| Pretraining mode | Dropout ratio | +| ------------------------ | ------------------------- | +| Determinstic | 0.1 | +| Stochastic | 0.12 - 0.14 | + +### Results + +Fine-tuning the model pre-trained usng DeepSpeed Transformer and the recepie in [DeepSpeed Fast-Bert Training](/fast_bert/) should yield F1 score of 90.5 and is expected to increase if you let the pre-training longer than suggested in the tutorial. diff --git a/docs/_tutorials/bert-pretraining.md b/docs/_tutorials/bert-pretraining.md old mode 100644 new mode 100755 index 18b6afe26f15..659c98be7e7f --- a/docs/_tutorials/bert-pretraining.md +++ b/docs/_tutorials/bert-pretraining.md @@ -3,12 +3,6 @@ title: "BERT Pre-training" excerpt: "" --- -**Note:** -This tutorial will be updated to include new details for reproducing the -recent 44-minute [BERT pre-training record](https://www.microsoft.com/en-us/research/blog/zero-2-deepspeed-shattering-barriers-of-deep-learning-speed-scale/). -Please check again soon! -{: .notice--warning} - In this tutorial we will apply DeepSpeed to pre-train the BERT (**B**idirectional **E**ncoder **R**epresentations from **T**ransformers), which is widely used for many Natural Language Processing (NLP) tasks. The @@ -29,6 +23,7 @@ We work from adaptations of We have forked this repo under [DeepSpeedExamples/bing_bert](https://github.com/microsoft/DeepSpeedExamples/tree/master/bing_bert) and made several modifications in their script: + * We adopted the modeling code from NVIDIA's BERT under `bing_bert/nvidia/`. * We extended the data pipeline from [Project Turing](https://msturing.org/) under `bing_bert/turing/`. @@ -246,13 +241,76 @@ modifications. We have included a modified `train.py` file called applied. + +### Enabling DeepSpeed's Transformer Kernel + +To enable the transformer kernel for higher performance, first add an argument +`--deepspeed_transformer_kernel` in `utils.py`, we can set it as `False` by +default, for easily turning on/off. + +```python + parser.add_argument('--deepspeed_transformer_kernel', + default=False, + action='store_true', + help='Use DeepSpeed transformer kernel to accelerate.') +``` +Then in the `BertEncoder` class of the modeling source file, instantiate +transformer layers using DeepSpeed transformer kernel as below. + +```python + if args.deepspeed_transformer_kernel: + from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig, DeepSpeedConfig + + if hasattr(args, 'deepspeed_config') and args.deepspeed_config: + ds_config = DeepSpeedConfig(args.deepspeed_config) + else: + raise RuntimeError('deepspeed_config is not found in args.') + + cuda_config = DeepSpeedTransformerConfig( + batch_size = ds_config.train_micro_batch_size_per_gpu, + max_seq_length = args.max_seq_length, + hidden_size = config.hidden_size, + heads = config.num_attention_heads, + attn_dropout_ratio = config.attention_probs_dropout_prob, + hidden_dropout_ratio = config.hidden_dropout_prob, + num_hidden_layers = config.num_hidden_layers, + initializer_range = config.initializer_range, + local_rank = args.local_rank if hasattr(args, 'local_rank') else -1, + seed = args.seed, + fp16 = ds_config.fp16_enabled, + pre_layer_norm=True, + attn_dropout_checkpoint=args.attention_dropout_checkpoint, + normalize_invertible=args.normalize_invertible, + gelu_checkpoint=args.gelu_checkpoint, + stochastic_mode=True) + + self.layer = nn.ModuleList([copy.deepcopy(DeepSpeedTransformerLayer(i, cuda_config)) for i in range(config.num_hidden_layers)]) + else: + layer = BertLayer(config) + self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]) +``` +All configuration settings come from the DeepSpeed configuration file and +command arguments and thus we must pass the `args` variable to here in this model. + +Note: + +1. `batch_size` is the maximum bath size of input data, all fine-tuning training data or prediction data shouldn't exceed this threshold, otherwise it will throw an exception. In the DeepSpeed configuration file micro batch size is defined as `train_micro_batch_size_per_gpu`, e.g. if it is set as 8 and prediction uses batch size of 12, we can use 12 as transformer kernel batch size, or using "--predict_batch_size" argument to set prediction batch size to 8 or a smaller number. +2. `local_rank` in DeepSpeedTransformerConfig is used to assign the transformer kernel to the correct device. Since the model already runs set_device() before here, so does not need to be set here. +3. `stochastic_mode` has higher performance when it is enabled, we enable it in pre-training, and disable it in fine-tuning. +4. The transformer kernel has its own parameters and so the checkpoint files + generated with transformer kernel must to be loaded by the model with + transformer kernel enabled (such as in fine-tuning). + +For more details about the transformer kernel, please see [DeepSpeed Transformer Kernel](/transformer_kernel/) and [DeepSpeed Fast-Bert Training](/fast_bert/). + + ### Start Training An example of launching `deepspeed_train.py` on four nodes with four GPUs each would be: ```bash deepspeed --num_nodes 4 \ deepspeed_train.py \ --deepspeed \ - --deepspeed_config deepspeed_bsz4096_adam_config.json + --deepspeed_config deepspeed_bsz4096_adam_config.json \ --cf /path-to-deepspeed/examples/tests/bing_bert/bert_large_adam_seq128.json \ --train_batch_size 4096 \ --max_seq_length 128 \ @@ -263,6 +321,7 @@ deepspeed --num_nodes 4 \ --delay_allreduce \ --max_steps 32 \ --print_steps 1 \ + --deepspeed_transformer_kernel \ --output_dir ``` See the [Getting Started](/getting-started/) guide for more information on @@ -270,57 +329,52 @@ launching DeepSpeed. ------ -## Reproducing BERT Training Results with DeepSpeed +## Reproducing Fastest BERT Training Results with DeepSpeed -Our BERT training result is competitive across the industry in terms of -achieving F1 score of 90.5 or better on the SQUAD 1.1 dev set: +We achieve the fastest BERT training time while remaining competitive across the industry in terms of achieving F1 score of 90.5 or better on the SQUAD 1.1 dev set. Please follow the [Fine-tuning](/bert-finetuning/) tutorial to finetune your model pretrained by transformer kernel and reprodue the SQUAD F1 score. +- We complete BERT pretraining in 44 minutes using 1024 V100 GPUs (64 NVIDIA DGX-2 nodes). In comparison, the previous SOTA from NVIDIA takes 47 mins using 1472 V100 GPUs. DeepSpeed is not only faster but also uses 30% less resources. Using the same 1024 GPUS, NVIDIA BERT is 52% slower than DeepSpeed, taking 67 minutes to train. - Comparing with the original BERT training time from Google, it took them -about 96 hours to reach parity on 64 TPU2 chips, while it took us 14 hours on +about 96 hours to reach parity on 64 TPU2 chips, while it took us less than 9 hours on 4 DGX-2 nodes of 64 V100 GPUs. -- On 256 GPUs, it took us 3.7 hours, faster than state-of-art result (3.9 +- On 256 GPUs, it took us 2.4, faster than state-of-art result (3.9 hours) from Nvidia using their superpod on the same number of GPUs ([link](https://devblogs.nvidia.com/training-bert-with-gpus/)). -![BERT Training Time](/assets/images/bert-large-training-time.png){: .align-center} +![DeepSpeed BERT Training Time](/assets/images/end-to-end-bert-training.png){: .align-center} Our configuration for the BERT training result above can be reproduced with -the scripts/json configs in our DeepSpeed repo. Below is a table containing a +the scripts/json configs in our DeepSpeedExamples repo. Below is a table containing a summary of the configurations. Specifically see the -`ds_train_bert_bsz16k_seq128.sh` and `ds_train_bert_bsz16k_seq512.sh` scripts +`ds_train_bert_bsz64k_seq128.sh` and `ds_train_bert_bsz32k_seq512.sh` scripts for more details in [DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples/tree/master/bing_bert). | Parameters | 128 Sequence | 512 Sequence | | ------------------------ | ------------------------- | ------------------------- | -| Total batch size | 16K | 16K | +| Total batch size | 64K | 32K | | Train micro batch size per gpu | 64 | 8 | | Optimizer | Lamb | Lamb | -| Learning rate | 4e-3 | 1e-3 | -| Min Lamb coefficient | 0.08 | 0.08 | -| Max Lamb coefficient | 0.5 | 0.5 | -| Learning rate scheduler | `warmup_linear_decay_exp` | `warmup_linear_decay_exp` | -| Warmup proportion | 0.02 | 0.01 | -| Decay rate | 0.90 | 0.70 | -| Decay step | 1000 | 1000 | -| Max Training steps | 187000 | 18700 | -| Rewarm LR | N/A | True | -| Output checkpoint number | 150 | 162 | -| Sample count | 402679081 | 34464170 | -| Iteration count | 24430 | 2089 | - - -## DeepSpeed Throughput Results - -We have measured the throughput results of DeepSpeed using both the Adam -optimizer and LAMB optimizer. We measure the throughput by measuring the wall -clock time to process one mini-batch and dividing the mini-batch size with -the elapsed wall clock time. The table below shows that for sequence length 128, -DeepSpeed achieves 200 samples/sec throughput on a single V100 GPU, and it -obtains 53X and 57.4X speedups over the single GPU run for Adam and LAMB -respectively: - -![](/assets/images/deepspeed-throughput-seq128.png){: .align-center} - -![](/assets/images/deepspeed-throughput-seq512.png){: .align-center} +| Learning rate | 11e-3 | 2e-3 | +| Initial learning rate (`lr_offset`) | 10e-4 | 0.0 | +| Min Lamb coefficient | 0.01 | 0.01 | +| Max Lamb coefficient | 0.3 | 0.3 | +| Learning rate scheduler | `warmup_exp_decay_exp` | `warmup_exp_decay_exp` | +| Warmup proportion | 0.02 | 0.02 | +| Decay rate | 0.90 | 0.90 | +| Decay step | 250 | 150 | +| Max training steps | 7500 | 7500 | +| Rewarm learning rate | N/A | True | +| Output checkpoint number | 150 | 160-162 | +| Sample count | 403M | 18-22M | +| Epoch count | 150 | 160-162 | + + +## DeepSpeed Single GPU Throughput Results + +![DeepSpeed Single GPU Bert Training Throughput](/assets/images/single-gpu-throughput.png){: .align-center} + +Compared to SOTA, DeepSpeed significantly improves single GPU performance for transformer-based model like BERT. Figure above shows the single GPU throughput of training BertBERT-Large optimized through DeepSpeed, compared with two well-known Pytorch implementations, NVIDIA BERT and HuggingFace BERT. DeepSpeed reaches as high as 64 and 53 teraflops throughputs (corresponding to 272 and 52 samples/second) for sequence lengths of 128 and 512, respectively, exhibiting up to 28% throughput improvements over NVIDIA BERT and up to 62% over HuggingFace BERT. We also support up to 1.8x larger batch size without running out of memory. + +For more details on how we achieve the record breaking BERT training time please check out deep dive into DeepSpeed BERT [Fastest BERT Training](https://www.deepspeed.ai/news/2020/05/18/bert-record.html) diff --git a/docs/_tutorials/transformer_kernel.md b/docs/_tutorials/transformer_kernel.md new file mode 100755 index 000000000000..394e91877a3b --- /dev/null +++ b/docs/_tutorials/transformer_kernel.md @@ -0,0 +1,124 @@ +--- +title: "DeepSpeed Transformer Kernel" +--- + +This tutorial shows how to enable the DeepSpeed transformer kernel and set its different configuration parameters. + +## DeepSpeed Transformer Kernel +Transformer layers are ubiquitous in many recent sequence-processing models, +such as Natural-Language-Processing. Thus, training transformer-based networks +requires to be highly efficient in term of performance, in order to allow scientists to +explore different models across various application domains in a reasonable amount of time. +To this end, we have developed a new kernel for transformer networks which includes several +optimizations specific to these layers, which boost the training throughput on single GPU and scales +well as we increase the number of GPUs. For more information on the details of transformer kernel, please visit our recent blog post on the [fastest Bert training](/fast_bert/). + +## Prerequisites + +To use transformer kernel for training a model, you should Integrate DeepSpeed into your training script using the [Getting Started](/getting-started/) guide. + +### **Integrate Transformer Kernel** + +First of all, you need to integrate transformer kernel into the top-level model. Here, we show an example of instantiating the transformer kernel using the Pre-LN BERT-Large configuration settings. This configuration has 24 layers with 1024 hidden-dimension and uses the sequence length of 128 and batch size of 64. To add all these layers, we copy the same layer specification `num_hidden_layer` times with different IDs inside a ModuleList. + +```python +config = DeepSpeedTransformerConfig(batch_size = 64, + max_seq_length = 128, + hidden_size = 1024, + heads = 16, + attn_dropout_ratio = 0.1, + hidden_dropout_ratio = 0.1, + num_hidden_layers = 24, + initializer_range = 0.02, + local_rank = 0, + seed = 1234, + fp16 = True, + pre_layer_norm=True, + attn_dropout_checkpoint=False, + normalize_invertible=False, + gelu_checkpoint=False) +self.layer = nn.ModuleList([ + copy.deepcopy(DeepSpeedTransformerLayer(i, cuda_config)) + for i in range(config.num_hidden_layers) +]) +``` +### Transformer kernel Parameters + +The transformer kernel is configured by a number of parameters which allow users to +explore different settings. We partition these parameters into three categories: + +1. General configuration, used by different types of transformer layers +2. Environment parameters, specifying the system's setting +3. High-performance flag, optimizing training with the stochastic computation +4. Memory optimization flags, trade off computing power for memory + +The general parameters for configuring the transformer kernel are: + +1. `batch_size`: The micro-batch size used for running the kernel on each GPU +2. `max_seq_length`: The sequence-length of the model being trained with DeepSpeed +3. `hidden_size`: The hidden size of the transformer layer +4. `heads`: The number of heads in the self-attention of the transformer layer +5. `attn_dropout_ratio`: The ratio of dropout for the attention's output +6. `hidden_dropout_ratio`: The ratio of dropout for the transformer's output +7. `num_hidden_layers`: The number of transformer layers +8. `pre_layer_norm`: Select between Pre-LN or Post-LN transformer architecture + +The environment parameters of the transformer kernel includes: + +1. `local_rank`: The rank of the current GPU running the transformer kernel +2. `seed`: The random seed for the dropout layer +3. `fp16`: Enable half-precision computation +4. `initializer_range`: Bert's initializer range + +High-performance optimization flag: + +1. `stochastic_mode`: By turning on this flag, the training can run faster by 2% on average. Note, that this flag has some level of non-determinism and can produce different results on different runs. However, we have seen that by enabling it, the pretraining tasks such as BERT are not affected and can obtain a high accuracy level. On the other hand, for the downstream tasks, such as fine-tuning, we recommend to turn it off in order to be able to reproduce the same result through the regular kernel execution. + +The memory-optimization flags consist of: + +1. `attn_dropout_checkpoint`: Enable checkpointing of attention dropout to save memory +2. `normalize_invertible`: Enable invertible LayerNorm execution (dropping the input activation) +3. `gelu_checkpoint`: Enable checkpointing of Gelu activation output to save memory + +To illustrate the required model configuration changes to use transformer kernel in model training, we use a Bert model and go through the different configurations in order to support the different sequence lengths and batch sizes. Please see the instruction at [Bert training tutorial](/bert-pretraining/). + +### **Memory Optimization Flags** + +We provide several techniques into the transformer kernel which saves the memory at different parts of a layer. We expose them as the configurable settings that can be enabled when calling the kernel. By turning on each of these optimization flags, we can support larger batch sizes. Even though we trade off performance for memory using some of these techniques, the end-to-end training efficiency increases by using the larger batch size. + +By setting the `normalize_invertible` flag, we force the kernel to drop the input activations to the normalize layers of transformer. We can do this since the kernel includes an optimization to compute the gradients of the parameters and the input to this layer by only using the output activations. + +The `attn_dropout_checkpoint` and `gelu_checkpoint` flags refer to the checkpointing approach, in which we drop the inputs to some parts of the transformer layer, attention dropout and Gelu, in order to save an important part of the activation memory. Based on our performance profiling, the performance cost of rematerializing these two are negligible and finally the performance benefit that we gain from running larger batch size compensate for that. + +The following table shows which memory optimization flags need to be turned on when running Bert-Large on NVIDIA V100 GPU with 32GB of memory, considering different micro-batch sizes and sequence lengths. For the two sequence lengths, 128 and 512, used in our experiments, we have seen that larger batch size improves the overall training performance for both. Please see our [Bert-Fast](/fast-bert/) blog post for more information regarding the performance evaluation of these configurations. + +| Micro-batch size | 128 sequence-length | 512 sequence-length | +| :--------------: | :-----------------------: | :--------------------------------------: | +| > 12 | - | `attn_dropout_checkpoint` | +| > 16 | - | `normalize_invertible`, `gelu_checkpoint`| +| > 80 | `normalize_invertible` | OOM | +| > 112 | `attn_dropout_checkpoint` | OOM | +| > 128 | `gelu_checkpoint` | OOM | + + +### **Enable Transformer Kernel** + +As mentioned earlier, in order to run the transformer network using the custom DeepSpeed kernel, we only need to pass the `deepspeed_transformer_kernel` option when running the training script. Below, we show an example of how we pass this parameter to the `deepspeed` launcher, besides the rest of parameters for the Bert pre-training task. + +```bash +deepspeed deepspeed_train.py \ +--cf bert_large_lamb.json \ +--max_seq_length 512 \ +--print_steps 100 \ +--deepspeed \ +--deepspeed_transformer_kernel \ +--deepspeed_config deepspeed_bsz32K_lamb_config_seq512.json \ +--rewarmup \ +--lr_schedule "EE" \ +--lr_offset 0.0 \ +--attention_dropout_checkpoint \ +--load_training_checkpoint ${CHECKPOINT_BASE_PATH} \ +--load_checkpoint_id ${CHECKPOINT_EPOCH150_NAME} + +``` +In addition to transformer kernel flag, we can specify the memory optimization settings as discussed earlier. As an example, we use the `attention_dropout_checkpoint` option here for running the sequence length 512, in order to run the micro-batch size of 16 at each GPU. 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a/docs/code-docs/source/checkpointing.rst b/docs/code-docs/source/checkpointing.rst deleted file mode 100644 index 07a1f6576c8b..000000000000 --- a/docs/code-docs/source/checkpointing.rst +++ /dev/null @@ -1,26 +0,0 @@ -DeepSpeed Activation Checkpointing -====================== - -The activation checkpointing API's in DeepSpeed can be used to enable a range of memory optimizations relating -to activation checkpointing. These include activation partitioning across -GPUs when using model parallelism, CPU Checkpointing, contiguous memory optimizations, etc. -Please see the `DeepSpeed JSON config <https://www.deepspeed.ai/docs/config-json/>`_ for the full set. - -Here we present the activation checkpointing API's. -Please see the enabling DeepSpeed for Megatron-LM tutorial for usage details. - -.. autofunction:: deepspeed.checkpointing.configure - -.. autofunction:: deepspeed.checkpointing.is_configured - -.. autofunction:: deepspeed.checkpointing.checkpoint - -.. autofunction:: deepspeed.checkpointing.reset - -.. autofunction:: deepspeed.checkpointing.get_cuda_rng_tracker - -.. autofunction:: deepspeed.checkpointing.model_parallel_cuda_manual_seed - -.. autoclass:: deepspeed.checkpointing.CudaRNGStatesTracker - -.. autoclass:: deepspeed.checkpointing.CheckpointFunction diff --git a/docs/code-docs/source/conf.py b/docs/code-docs/source/conf.py index 581728f08cd9..17f66d2a3438 100644 --- a/docs/code-docs/source/conf.py +++ b/docs/code-docs/source/conf.py @@ -76,4 +76,11 @@ autoclass_content = 'both' -autodoc_mock_imports = ["torch", "apex", "mpi4py", "tensorboardX"] +autodoc_mock_imports = [ + "torch", + "apex", + "mpi4py", + "tensorboardX", + "deepspeed_transformer_cuda", + "deepspeed_stochastic_transformer_cuda", +] diff --git a/docs/code-docs/source/index.rst b/docs/code-docs/source/index.rst index 3953b7d9d412..59d53b09912f 100644 --- a/docs/code-docs/source/index.rst +++ b/docs/code-docs/source/index.rst @@ -8,7 +8,6 @@ Model Setup :maxdepth: 2 initialize - checkpointing Training API ------------ @@ -28,6 +27,14 @@ Checkpointing API activation-checkpointing +Transformer Kernel API +---------------------- +.. toctree:: + :maxdepth: 2 + + kernel + + Indices and tables ------------------ diff --git a/docs/code-docs/source/kernel.rst b/docs/code-docs/source/kernel.rst new file mode 100644 index 000000000000..131708132c3e --- /dev/null +++ b/docs/code-docs/source/kernel.rst @@ -0,0 +1,17 @@ +Transformer Kernels +=================== + +The transformer kernel API in DeepSpeed can be used to create BERT transformer layer for +more efficient pre-training and fine-tuning, it includes the transformer layer configurations and +transformer layer module initialization. + +Here we present the transformer kernel API. +Please see the `BERT pre-training tutorial <https://www.deepspeed.ai/tutorials/bert-pretraining/>`_ for usage details. + +DeepSpeed Transformer Config +---------------------------- +.. autoclass:: deepspeed.DeepSpeedTransformerConfig + +DeepSpeed Transformer Layer +---------------------------- +.. autoclass:: deepspeed.DeepSpeedTransformerLayer diff --git a/requirements.txt b/requirements.txt index 6ac3b2a446a0..ac94d73c25a8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,3 +8,4 @@ tensorflow-gpu==1.15.2 pytest pytest-forked pre-commit +clang-format diff --git a/setup.py b/setup.py old mode 100644 new mode 100755 index 7957e371bf22..cc1484177b24 --- a/setup.py +++ b/setup.py @@ -10,12 +10,10 @@ import os import torch -from deepspeed import __version__ as ds_version from setuptools import setup, find_packages from torch.utils.cpp_extension import CUDAExtension, BuildExtension cmdclass = {} -ext_modules = [] cmdclass['build_ext'] = BuildExtension TORCH_MAJOR = int(torch.__version__.split('.')[0]) @@ -42,20 +40,84 @@ version_ge_1_5 = ['-DVERSION_GE_1_5'] version_dependent_macros = version_ge_1_1 + version_ge_1_3 + version_ge_1_5 -ext_modules.append( - CUDAExtension(name='fused_lamb_cuda', - sources=['csrc/fused_lamb_cuda.cpp', - 'csrc/fused_lamb_cuda_kernel.cu'], +ext_modules = [ + CUDAExtension( + name='deepspeed_lamb_cuda', + sources=['csrc/lamb/fused_lamb_cuda.cpp', + 'csrc/lamb/fused_lamb_cuda_kernel.cu'], + include_dirs=['csrc/includes'], + extra_compile_args={ + 'cxx': [ + '-O3', + ] + version_dependent_macros, + 'nvcc': ['-O3', + '--use_fast_math'] + version_dependent_macros + }), + CUDAExtension(name='deepspeed_transformer_cuda', + sources=[ + 'csrc/transformer/ds_transformer_cuda.cpp', + 'csrc/transformer/cublas_wrappers.cu', + 'csrc/transformer/transform_kernels.cu', + 'csrc/transformer/gelu_kernels.cu', + 'csrc/transformer/dropout_kernels.cu', + 'csrc/transformer/normalize_kernels.cu', + 'csrc/transformer/softmax_kernels.cu', + 'csrc/transformer/general_kernels.cu' + ], + include_dirs=['csrc/includes'], extra_compile_args={ - 'cxx': [ + 'cxx': ['-O3', + '-std=c++14', + '-g', + '-Wno-reorder'], + 'nvcc': [ '-O3', - ] + version_dependent_macros, - 'nvcc': ['-O3', - '--use_fast_math'] + version_dependent_macros - })) + '--use_fast_math', + '-gencode', + 'arch=compute_61,code=compute_61', + '-gencode', + 'arch=compute_70,code=compute_70', + '-std=c++14', + '-U__CUDA_NO_HALF_OPERATORS__', + '-U__CUDA_NO_HALF_CONVERSIONS__', + '-U__CUDA_NO_HALF2_OPERATORS__' + ] + }), + CUDAExtension(name='deepspeed_stochastic_transformer_cuda', + sources=[ + 'csrc/transformer/ds_transformer_cuda.cpp', + 'csrc/transformer/cublas_wrappers.cu', + 'csrc/transformer/transform_kernels.cu', + 'csrc/transformer/gelu_kernels.cu', + 'csrc/transformer/dropout_kernels.cu', + 'csrc/transformer/normalize_kernels.cu', + 'csrc/transformer/softmax_kernels.cu', + 'csrc/transformer/general_kernels.cu' + ], + include_dirs=['csrc/includes'], + extra_compile_args={ + 'cxx': ['-O3', + '-std=c++14', + '-g', + '-Wno-reorder'], + 'nvcc': [ + '-O3', + '--use_fast_math', + '-gencode', + 'arch=compute_61,code=compute_61', + '-gencode', + 'arch=compute_70,code=compute_70', + '-std=c++14', + '-U__CUDA_NO_HALF_OPERATORS__', + '-U__CUDA_NO_HALF_CONVERSIONS__', + '-U__CUDA_NO_HALF2_OPERATORS__', + '-D__STOCHASTIC_MODE__' + ] + }), +] setup(name='deepspeed', - version=ds_version, + version='0.2.0', description='DeepSpeed library', author='DeepSpeed Team', author_email='deepspeed@microsoft.com', diff --git a/tests/unit/modeling.py b/tests/unit/modeling.py new file mode 100755 index 000000000000..09e1eccc61c6 --- /dev/null +++ b/tests/unit/modeling.py @@ -0,0 +1,1578 @@ +# DeepSpeed note, code taken from commit 3d59216cec89a363649b4fe3d15295ba936ced0f +# https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/modeling.py + +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch BERT model.""" + +from __future__ import absolute_import, division, print_function, unicode_literals + +import copy +import json +import logging +import math +import os +import shutil +import tarfile +import tempfile +import sys +from io import open + +import torch +from torch import nn +from torch.nn import CrossEntropyLoss +from torch.utils import checkpoint +import torch.distributed as dist + +from torch.nn import Module +from torch.nn.parameter import Parameter +import torch.nn.functional as F +import torch.nn.init as init +import time + +#from numba import cuda + +#from deepspeed_cuda import DeepSpeedSoftmaxConfig, DeepSpeedSoftmax + +logger = logging.getLogger(__name__) + +PRETRAINED_MODEL_ARCHIVE_MAP = { + 'bert-base-uncased': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz", + 'bert-large-uncased': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz", + 'bert-base-cased': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz", + 'bert-large-cased': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz", + 'bert-base-multilingual-uncased': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz", + 'bert-base-multilingual-cased': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz", + 'bert-base-chinese': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz", +} +CONFIG_NAME = 'bert_config.json' +WEIGHTS_NAME = 'pytorch_model.bin' +TF_WEIGHTS_NAME = 'model.ckpt' + + +def load_tf_weights_in_bert(model, tf_checkpoint_path): + """ Load tf checkpoints in a pytorch model + """ + try: + import re + import numpy as np + import tensorflow as tf + except ImportError: + print( + "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see " + "https://www.tensorflow.org/install/ for installation instructions.") + raise + tf_path = os.path.abspath(tf_checkpoint_path) + print("Converting TensorFlow checkpoint from {}".format(tf_path)) + # Load weights from TF model + init_vars = tf.train.list_variables(tf_path) + names = [] + arrays = [] + for name, shape in init_vars: + print("Loading TF weight {} with shape {}".format(name, shape)) + array = tf.train.load_variable(tf_path, name) + names.append(name) + arrays.append(array) + + for name, array in zip(names, arrays): + name = name.split('/') + # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v + # which are not required for using pretrained model + if any(n in ["adam_v", "adam_m"] for n in name): + print("Skipping {}".format("/".join(name))) + continue + pointer = model + for m_name in name: + if re.fullmatch(r'[A-Za-z]+_\d+', m_name): + l = re.split(r'_(\d+)', m_name) + else: + l = [m_name] + if l[0] == 'kernel' or l[0] == 'gamma': + pointer = getattr(pointer, 'weight') + elif l[0] == 'output_bias' or l[0] == 'beta': + pointer = getattr(pointer, 'bias') + elif l[0] == 'output_weights': + pointer = getattr(pointer, 'weight') + else: + pointer = getattr(pointer, l[0]) + if len(l) >= 2: + num = int(l[1]) + pointer = pointer[num] + if m_name[-11:] == '_embeddings': + pointer = getattr(pointer, 'weight') + elif m_name == 'kernel': + array = np.transpose(array) + try: + assert pointer.shape == array.shape + except AssertionError as e: + e.args += (pointer.shape, array.shape) + raise + print("Initialize PyTorch weight {}".format(name)) + pointer.data = torch.from_numpy(array) + return model + + +@torch.jit.script +def f_gelu(x): + return x * 0.5 * (1.0 + torch.erf(x / 1.41421)) + + +@torch.jit.script +def bias_gelu(bias, y): + x = bias + y + return x * 0.5 * (1.0 + torch.erf(x / 1.41421)) + + +@torch.jit.script +def bias_tanh(bias, y): + x = bias + y + return torch.tanh(x) + + +def gelu(x): + """Implementation of the gelu activation function. + For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): + 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) + Also see https://arxiv.org/abs/1606.08415 + """ + return f_gelu(x) + + +def swish(x): + return x * torch.sigmoid(x) + + +ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish} + + +class GPUTimer: + def __init__(self): + super().__init__() + self.start = cuda.event() + self.stop = cuda.event() + + def record(self): + self.start.record() + + def elapsed(self): + self.stop.record() + self.stop.synchronize() + return self.start.elapsed_time(self.stop) / 1000.0 + + +class LinearActivation(Module): + r"""Fused Linear and activation Module. + """ + __constants__ = ['bias'] + + def __init__(self, + in_features, + out_features, + weights, + biases, + act='gelu', + bias=True): + super(LinearActivation, self).__init__() + self.in_features = in_features + self.out_features = out_features + self.fused_gelu = False + self.fused_tanh = False + if isinstance(act, + str) or (sys.version_info[0] == 2 and isinstance(act, + unicode)): + if bias and act == 'gelu': + self.fused_gelu = True + elif bias and act == 'tanh': + self.fused_tanh = True + else: + self.act_fn = ACT2FN[act] + else: + self.act_fn = act + #self.weight = Parameter(torch.Tensor(out_features, in_features)) + self.weight = weights[5] + self.bias = biases[5] + #if bias: + # self.bias = Parameter(torch.Tensor(out_features)) + #else: + # self.register_parameter('bias', None) + #self.reset_parameters() + + def reset_parameters(self): + init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + if self.bias is not None: + fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight) + bound = 1 / math.sqrt(fan_in) + init.uniform_(self.bias, -bound, bound) + + def forward(self, input): + if self.fused_gelu: + #timing = [] + #t1 = GPUTimer() + #t1.record() + y = F.linear(input, self.weight, None) + #timing.append(t1.elapsed()) + #t1.record() + bg = bias_gelu(self.bias, y) + #timing.append(t1.elapsed()) + return bg + elif self.fused_tanh: + return bias_tanh(self.bias, F.linear(input, self.weight, None)) + else: + return self.act_fn(F.linear(input, self.weight, self.bias)) + + def extra_repr(self): + return 'in_features={}, out_features={}, bias={}'.format( + self.in_features, + self.out_features, + self.bias is not None) + + +class BertConfig(object): + """Configuration class to store the configuration of a `BertModel`. + """ + def __init__(self, + vocab_size_or_config_json_file, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + batch_size=8, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=512, + type_vocab_size=2, + initializer_range=0.02, + fp16=False): + """Constructs BertConfig. + + Args: + vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`. + hidden_size: Size of the encoder layers and the pooler layer. + num_hidden_layers: Number of hidden layers in the Transformer encoder. + num_attention_heads: Number of attention heads for each attention layer in + the Transformer encoder. + intermediate_size: The size of the "intermediate" (i.e., feed-forward) + layer in the Transformer encoder. + hidden_act: The non-linear activation function (function or string) in the + encoder and pooler. If string, "gelu", "relu" and "swish" are supported. + hidden_dropout_prob: The dropout probabilitiy for all fully connected + layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob: The dropout ratio for the attention + probabilities. + max_position_embeddings: The maximum sequence length that this model might + ever be used with. Typically set this to something large just in case + (e.g., 512 or 1024 or 2048). + type_vocab_size: The vocabulary size of the `token_type_ids` passed into + `BertModel`. + initializer_range: The sttdev of the truncated_normal_initializer for + initializing all weight matrices. + """ + if isinstance(vocab_size_or_config_json_file, + str) or (sys.version_info[0] == 2 + and isinstance(vocab_size_or_config_json_file, + unicode)): + with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader: + json_config = json.loads(reader.read()) + for key, value in json_config.items(): + self.__dict__[key] = value + elif isinstance(vocab_size_or_config_json_file, int): + self.vocab_size = vocab_size_or_config_json_file + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.batch_size = batch_size + self.hidden_act = hidden_act + self.intermediate_size = intermediate_size + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.type_vocab_size = type_vocab_size + self.initializer_range = initializer_range + self.fp16 = fp16 + else: + raise ValueError("First argument must be either a vocabulary size (int)" + "or the path to a pretrained model config file (str)") + + @classmethod + def from_dict(cls, json_object): + """Constructs a `BertConfig` from a Python dictionary of parameters.""" + config = BertConfig(vocab_size_or_config_json_file=-1) + for key, value in json_object.items(): + config.__dict__[key] = value + return config + + @classmethod + def from_json_file(cls, json_file): + """Constructs a `BertConfig` from a json file of parameters.""" + with open(json_file, "r", encoding='utf-8') as reader: + text = reader.read() + return cls.from_dict(json.loads(text)) + + def __repr__(self): + return str(self.to_json_string()) + + def to_dict(self): + """Serializes this instance to a Python dictionary.""" + output = copy.deepcopy(self.__dict__) + return output + + def to_json_string(self): + """Serializes this instance to a JSON string.""" + return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" + + +try: + import apex + #apex.amp.register_half_function(apex.normalization.fused_layer_norm, 'FusedLayerNorm') + import apex.normalization + #apex.amp.register_float_function(apex.normalization.FusedLayerNorm, 'forward') + BertLayerNorm = apex.normalization.FusedLayerNorm +except ImportError: + print( + "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex." + ) + + class BertLayerNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-12): + """Construct a layernorm module in the TF style (epsilon inside the square root). + """ + super(BertLayerNorm, self).__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.bias = nn.Parameter(torch.zeros(hidden_size)) + self.variance_epsilon = eps + + def forward(self, x): + u = x.mean(-1, keepdim=True) + s = (x - u).pow(2).mean(-1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.variance_epsilon) + return self.weight * x + self.bias + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings. + """ + def __init__(self, config): + super(BertEmbeddings, self).__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, + config.hidden_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, + config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, input_ids, token_type_ids=None): + seq_length = input_ids.size(1) + position_ids = torch.arange(seq_length, + dtype=torch.long, + device=input_ids.device) + position_ids = position_ids.unsqueeze(0).expand_as(input_ids) + if token_type_ids is None: + token_type_ids = torch.zeros_like(input_ids) + + words_embeddings = self.word_embeddings(input_ids) + position_embeddings = self.position_embeddings(position_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = words_embeddings + position_embeddings + token_type_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertSelfAttention(nn.Module): + def __init__(self, i, config, weights, biases): + super(BertSelfAttention, self).__init__() + if config.hidden_size % config.num_attention_heads != 0: + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, + config.num_attention_heads)) + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.query.weight = weights[0] + self.query.bias = biases[0] + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.key.weight = weights[1] + self.key.bias = biases[1] + self.value = nn.Linear(config.hidden_size, self.all_head_size) + self.value.weight = weights[2] + self.value.bias = biases[2] + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.softmax = nn.Softmax(dim=-1) + #self.softmax_config = DeepSpeedSoftmaxConfig() + #self.softmax_config.batch_size = config.batch_size + #self.softmax_config.max_seq_length = config.max_position_embeddings + #self.softmax_config.hidden_size = config.hidden_size + #self.softmax_config.heads = config.num_attention_heads + #self.softmax_config.softmax_id = i + #self.softmax_config.fp16 = config.fp16 + #self.softmax_config.prob_drop_out = 0.0 + #self.softmax = DeepSpeedSoftmax(i, self.softmax_config) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, + self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def transpose_key_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, + self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 3, 1) + + def forward(self, hidden_states, attention_mask, grads=None): + + mixed_query_layer = self.query(hidden_states) + + mixed_key_layer = self.key(hidden_states) + + mixed_value_layer = self.value(hidden_states) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + key_layer = self.transpose_key_for_scores(mixed_key_layer) + + value_layer = self.transpose_for_scores(mixed_value_layer) + + attention_scores = torch.matmul(query_layer, key_layer) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + attention_scores = attention_scores + attention_mask + attention_probs = self.softmax(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + context_layer = torch.matmul(attention_probs, value_layer) + context_layer1 = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer1.size()[:-2] + (self.all_head_size, ) + context_layer1 = context_layer1.view(*new_context_layer_shape) + + return context_layer1 + + +class BertSelfOutput(nn.Module): + def __init__(self, config, weights, biases): + super(BertSelfOutput, self).__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dense.weight = weights[3] + self.dense.bias = biases[3] + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + def get_w(self): + return self.dense.weight + + +class BertAttention(nn.Module): + def __init__(self, i, config, weights, biases): + super(BertAttention, self).__init__() + self.self = BertSelfAttention(i, config, weights, biases) + self.output = BertSelfOutput(config, weights, biases) + + def forward(self, input_tensor, attention_mask): + self_output = self.self(input_tensor, attention_mask) + attention_output = self.output(self_output, input_tensor) + return attention_output + + def get_w(self): + return self.output.get_w() + + +class BertIntermediate(nn.Module): + def __init__(self, config, weights, biases): + super(BertIntermediate, self).__init__() + self.dense_act = LinearActivation(config.hidden_size, + config.intermediate_size, + weights, + biases, + act=config.hidden_act) + + def forward(self, hidden_states): + hidden_states = self.dense_act(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config, weights, biases): + super(BertOutput, self).__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dense.weight = weights[6] + self.dense.bias = biases[6] + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertLayer(nn.Module): + def __init__(self, i, config, weights, biases): + super(BertLayer, self).__init__() + self.attention = BertAttention(i, config, weights, biases) + self.intermediate = BertIntermediate(config, weights, biases) + self.output = BertOutput(config, weights, biases) + self.weight = weights + self.biases = biases + + def forward(self, hidden_states, attention_mask, grads, collect_all_grads=False): + attention_output = self.attention(hidden_states, attention_mask) + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + + if collect_all_grads: + # self.weight[0].register_hook(lambda x, self=self: grads.append([x,"Q_W"])) + # self.biases[0].register_hook(lambda x, self=self: grads.append([x,"Q_B"])) + # self.weight[1].register_hook(lambda x, self=self: grads.append([x,"K_W"])) + # self.biases[1].register_hook(lambda x, self=self: grads.append([x,"K_B"])) + self.weight[2].register_hook(lambda x, self=self: grads.append([x, "V_W"])) + self.biases[2].register_hook(lambda x, self=self: grads.append([x, "V_B"])) + self.weight[3].register_hook(lambda x, self=self: grads.append([x, "O_W"])) + self.biases[3].register_hook(lambda x, self=self: grads.append([x, "O_B"])) + self.attention.output.LayerNorm.weight.register_hook( + lambda x, + self=self: grads.append([x, + "N2_W"])) + self.attention.output.LayerNorm.bias.register_hook( + lambda x, + self=self: grads.append([x, + "N2_B"])) + self.weight[5].register_hook(lambda x, self=self: grads.append([x, "int_W"])) + self.biases[5].register_hook(lambda x, self=self: grads.append([x, "int_B"])) + self.weight[6].register_hook(lambda x, self=self: grads.append([x, "out_W"])) + self.biases[6].register_hook(lambda x, self=self: grads.append([x, "out_B"])) + self.output.LayerNorm.weight.register_hook( + lambda x, + self=self: grads.append([x, + "norm_W"])) + self.output.LayerNorm.bias.register_hook( + lambda x, + self=self: grads.append([x, + "norm_B"])) + + return layer_output + + def get_w(self): + return self.attention.get_w() + + +class BertEncoder(nn.Module): + def __init__(self, config, weights, biases): + super(BertEncoder, self).__init__() + #layer = BertLayer(config, weights, biases) + self.FinalLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + + self.layer = nn.ModuleList([ + copy.deepcopy(BertLayer(i, + config, + weights, + biases)) for i in range(config.num_hidden_layers) + ]) + self.grads = [] + self.graph = [] + + def get_grads(self): + return self.grads + + # def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True): + # all_encoder_layers = [] + # for layer_module in self.layer: + # hidden_states = layer_module(hidden_states, attention_mask) + # if output_all_encoded_layers: + # all_encoder_layers.append(hidden_states) + # if not output_all_encoded_layers: + # all_encoder_layers.append(hidden_states) + # return all_encoder_layers + + def get_modules(self, big_node, input): + for mdl in big_node.named_children(): + graph.append(mdl) + get_modules(self, mdl, input) + + def forward(self, + hidden_states, + attention_mask, + output_all_encoded_layers=True, + checkpoint_activations=False): + all_encoder_layers = [] + + def custom(start, end): + def custom_forward(*inputs): + layers = self.layer[start:end] + x_ = inputs[0] + for layer in layers: + x_ = layer(x_, inputs[1]) + return x_ + + return custom_forward + + if checkpoint_activations: + l = 0 + num_layers = len(self.layer) + chunk_length = math.ceil(math.sqrt(num_layers)) + while l < num_layers: + hidden_states = checkpoint.checkpoint(custom(l, + l + chunk_length), + hidden_states, + attention_mask * 1) + l += chunk_length + # decoder layers + else: + for i, layer_module in enumerate(self.layer): + hidden_states = layer_module(hidden_states, + attention_mask, + self.grads, + collect_all_grads=True) + hidden_states.register_hook( + lambda x, + i=i, + self=self: self.grads.append([x, + "hidden_state"])) + #print("pytorch weight is: ", layer_module.get_w()) + + if output_all_encoded_layers: + all_encoder_layers.append((hidden_states)) + + if not output_all_encoded_layers or checkpoint_activations: + all_encoder_layers.append((hidden_states)) + return all_encoder_layers + + +#class BertEncoder(nn.Module): +# def __init__(self, config): +# super(BertEncoder, self).__init__() +# layer = BertLayer(config) +# self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]) +# +# def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True): +# all_encoder_layers = [] +# for layer_module in self.layer: +# hidden_states = layer_module(hidden_states, attention_mask) +# if output_all_encoded_layers: +# all_encoder_layers.append(hidden_states) +# if not output_all_encoded_layers: +# all_encoder_layers.append(hidden_states) +# return all_encoder_layers + + +class BertPooler(nn.Module): + def __init__(self, config): + super(BertPooler, self).__init__() + self.dense_act = LinearActivation(config.hidden_size, + config.hidden_size, + act="tanh") + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense_act(first_token_tensor) + return pooled_output + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super(BertPredictionHeadTransform, self).__init__() + self.dense_act = LinearActivation(config.hidden_size, + config.hidden_size, + act=config.hidden_act) + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + + def forward(self, hidden_states): + hidden_states = self.dense_act(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config, bert_model_embedding_weights): + super(BertLMPredictionHead, self).__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(bert_model_embedding_weights.size(1), + bert_model_embedding_weights.size(0), + bias=False) + self.decoder.weight = bert_model_embedding_weights + self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0))) + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + torch.cuda.nvtx.range_push( + "decoder input.size() = {}, weight.size() = {}".format( + hidden_states.size(), + self.decoder.weight.size())) + hidden_states = self.decoder(hidden_states) + self.bias + torch.cuda.nvtx.range_pop() + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config, bert_model_embedding_weights): + super(BertOnlyMLMHead, self).__init__() + self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class BertOnlyNSPHead(nn.Module): + def __init__(self, config): + super(BertOnlyNSPHead, self).__init__() + self.seq_relationship = nn.Linear(config.hidden_size, 2) + + def forward(self, pooled_output): + seq_relationship_score = self.seq_relationship(pooled_output) + return seq_relationship_score + + +class BertPreTrainingHeads(nn.Module): + def __init__(self, config, bert_model_embedding_weights): + super(BertPreTrainingHeads, self).__init__() + self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights) + self.seq_relationship = nn.Linear(config.hidden_size, 2) + + def forward(self, sequence_output, pooled_output): + prediction_scores = self.predictions(sequence_output) + seq_relationship_score = self.seq_relationship(pooled_output) + return prediction_scores, seq_relationship_score + + +class BertPreTrainedModel(nn.Module): + """ An abstract class to handle weights initialization and + a simple interface for dowloading and loading pretrained models. + """ + def __init__(self, config, *inputs, **kwargs): + super(BertPreTrainedModel, self).__init__() + if not isinstance(config, BertConfig): + raise ValueError( + "Parameter config in `{}(config)` should be an instance of class `BertConfig`. " + "To create a model from a Google pretrained model use " + "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format( + self.__class__.__name__, + self.__class__.__name__)) + self.config = config + + def init_bert_weights(self, module): + """ Initialize the weights. + """ + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, BertLayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + @classmethod + def from_pretrained(cls, + pretrained_model_name_or_path, + state_dict=None, + cache_dir=None, + from_tf=False, + *inputs, + **kwargs): + """ + Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict. + Download and cache the pre-trained model file if needed. + + Params: + pretrained_model_name_or_path: either: + - a str with the name of a pre-trained model to load selected in the list of: + . `bert-base-uncased` + . `bert-large-uncased` + . `bert-base-cased` + . `bert-large-cased` + . `bert-base-multilingual-uncased` + . `bert-base-multilingual-cased` + . `bert-base-chinese` + - a path or url to a pretrained model archive containing: + . `bert_config.json` a configuration file for the model + . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance + - a path or url to a pretrained model archive containing: + . `bert_config.json` a configuration file for the model + . `model.chkpt` a TensorFlow checkpoint + from_tf: should we load the weights from a locally saved TensorFlow checkpoint + cache_dir: an optional path to a folder in which the pre-trained models will be cached. + state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models + *inputs, **kwargs: additional input for the specific Bert class + (ex: num_labels for BertForSequenceClassification) + """ + if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP: + archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path] + else: + archive_file = pretrained_model_name_or_path + if resolved_archive_file == archive_file: + logger.info("loading archive file {}".format(archive_file)) + else: + logger.info("loading archive file {} from cache at {}".format( + archive_file, + resolved_archive_file)) + tempdir = None + if os.path.isdir(resolved_archive_file) or from_tf: + serialization_dir = resolved_archive_file + else: + # Extract archive to temp dir + tempdir = tempfile.mkdtemp() + logger.info("extracting archive file {} to temp dir {}".format( + resolved_archive_file, + tempdir)) + with tarfile.open(resolved_archive_file, 'r:gz') as archive: + archive.extractall(tempdir) + serialization_dir = tempdir + # Load config + config_file = os.path.join(serialization_dir, CONFIG_NAME) + config = BertConfig.from_json_file(config_file) + logger.info("Model config {}".format(config)) + # Instantiate model. + model = cls(config, *inputs, **kwargs) + if state_dict is None and not from_tf: + weights_path = os.path.join(serialization_dir, WEIGHTS_NAME) + state_dict = torch.load( + weights_path, + map_location='cpu' if not torch.cuda.is_available() else None) + if tempdir: + # Clean up temp dir + shutil.rmtree(tempdir) + if from_tf: + # Directly load from a TensorFlow checkpoint + weights_path = os.path.join(serialization_dir, TF_WEIGHTS_NAME) + return load_tf_weights_in_bert(model, weights_path) + # Load from a PyTorch state_dict + old_keys = [] + new_keys = [] + for key in state_dict.keys(): + new_key = None + if 'gamma' in key: + new_key = key.replace('gamma', 'weight') + if 'beta' in key: + new_key = key.replace('beta', 'bias') + if new_key: + old_keys.append(key) + new_keys.append(new_key) + for old_key, new_key in zip(old_keys, new_keys): + state_dict[new_key] = state_dict.pop(old_key) + + missing_keys = [] + unexpected_keys = [] + error_msgs = [] + # copy state_dict so _load_from_state_dict can modify it + metadata = getattr(state_dict, '_metadata', None) + state_dict = state_dict.copy() + if metadata is not None: + state_dict._metadata = metadata + + def load(module, prefix=''): + local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) + module._load_from_state_dict(state_dict, + prefix, + local_metadata, + True, + missing_keys, + unexpected_keys, + error_msgs) + for name, child in module._modules.items(): + if child is not None: + load(child, prefix + name + '.') + + start_prefix = '' + if not hasattr(model, + 'bert') and any(s.startswith('bert.') for s in state_dict.keys()): + start_prefix = 'bert.' + load(model, prefix=start_prefix) + if len(missing_keys) > 0: + logger.info("Weights of {} not initialized from pretrained model: {}".format( + model.__class__.__name__, + missing_keys)) + if len(unexpected_keys) > 0: + logger.info("Weights from pretrained model not used in {}: {}".format( + model.__class__.__name__, + unexpected_keys)) + if len(error_msgs) > 0: + raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( + model.__class__.__name__, + "\n\t".join(error_msgs))) + return model + + +class BertModel(BertPreTrainedModel): + """BERT model ("Bidirectional Embedding Representations from a Transformer"). + + Params: + config: a BertConfig class instance with the configuration to build a new model + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`. + + Outputs: Tuple of (encoded_layers, pooled_output) + `encoded_layers`: controled by `output_all_encoded_layers` argument: + - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end + of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each + encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size], + - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding + to the last attention block of shape [batch_size, sequence_length, hidden_size], + `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a + classifier pretrained on top of the hidden state associated to the first character of the + input (`CLS`) to train on the Next-Sentence task (see BERT's paper). + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = modeling.BertModel(config=config) + all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertModel, self).__init__(config) + self.embeddings = BertEmbeddings(config) + self.encoder = BertEncoder(config) + self.pooler = BertPooler(config) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + output_all_encoded_layers=True, + checkpoint_activations=False): + if attention_mask is None: + attention_mask = torch.ones_like(input_ids) + if token_type_ids is None: + token_type_ids = torch.zeros_like(input_ids) + + # We create a 3D attention mask from a 2D tensor mask. + # Sizes are [batch_size, 1, 1, to_seq_length] + # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] + # this attention mask is more simple than the triangular masking of causal attention + # used in OpenAI GPT, we just need to prepare the broadcast dimension here. + extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to(dtype=next( + self.parameters()).dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + + embedding_output = self.embeddings(input_ids, token_type_ids) + encoded_layers = self.encoder( + embedding_output, + extended_attention_mask, + output_all_encoded_layers=output_all_encoded_layers, + checkpoint_activations=checkpoint_activations) + sequence_output = encoded_layers[-1] + pooled_output = self.pooler(sequence_output) + if not output_all_encoded_layers: + encoded_layers = encoded_layers[-1] + return encoded_layers, pooled_output + + +class BertForPreTraining(BertPreTrainedModel): + """BERT model with pre-training heads. + This module comprises the BERT model followed by the two pre-training heads: + - the masked language modeling head, and + - the next sentence classification head. + + Params: + config: a BertConfig class instance with the configuration to build a new model. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `masked_lm_labels`: optional masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] + with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss + is only computed for the labels set in [0, ..., vocab_size] + `next_sentence_label`: optional next sentence classification loss: torch.LongTensor of shape [batch_size] + with indices selected in [0, 1]. + 0 => next sentence is the continuation, 1 => next sentence is a random sentence. + + Outputs: + if `masked_lm_labels` and `next_sentence_label` are not `None`: + Outputs the total_loss which is the sum of the masked language modeling loss and the next + sentence classification loss. + if `masked_lm_labels` or `next_sentence_label` is `None`: + Outputs a tuple comprising + - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and + - the next sentence classification logits of shape [batch_size, 2]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = BertForPreTraining(config) + masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, args): + super(BertForPreTraining, self).__init__(config) + self.summary_writer = None + if dist.get_rank() == 0: + self.summary_writer = args.summary_writer + self.samples_per_step = dist.get_world_size() * args.train_batch_size + self.sample_count = self.samples_per_step + self.bert = BertModel(config) + self.cls = BertPreTrainingHeads(config, + self.bert.embeddings.word_embeddings.weight) + self.apply(self.init_bert_weights) + + def log_summary_writer(self, logs: dict, base='Train'): + if dist.get_rank() == 0: + module_name = "Samples" #self._batch_module_name.get(batch_type, self._get_batch_type_error(batch_type)) + for key, log in logs.items(): + self.summary_writer.add_scalar(f'{base}/{module_name}/{key}', + log, + self.sample_count) + self.sample_count += self.samples_per_step + + def forward(self, batch, log=True): + #input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, next_sentence_label=None, checkpoint_activations=False): + input_ids = batch[1] + token_type_ids = batch[3] + attention_mask = batch[2] + masked_lm_labels = batch[5] + next_sentence_label = batch[4] + checkpoint_activations = False + + sequence_output, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, + output_all_encoded_layers=False, checkpoint_activations=checkpoint_activations) + prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) + + if masked_lm_labels is not None and next_sentence_label is not None: + loss_fct = CrossEntropyLoss(ignore_index=-1) + masked_lm_loss = loss_fct(prediction_scores.view(-1, + self.config.vocab_size), + masked_lm_labels.view(-1)) + next_sentence_loss = loss_fct(seq_relationship_score.view(-1, + 2), + next_sentence_label.view(-1)) + #print("loss is {} {}".format(masked_lm_loss, next_sentence_loss)) + total_loss = masked_lm_loss + next_sentence_loss + # if log: + # self.log_summary_writer(logs={'train_loss': total_loss.item()}) + return total_loss + else: + return prediction_scores, seq_relationship_score + + +class BertForMaskedLM(BertPreTrainedModel): + """BERT model with the masked language modeling head. + This module comprises the BERT model followed by the masked language modeling head. + + Params: + config: a BertConfig class instance with the configuration to build a new model. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] + with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss + is only computed for the labels set in [0, ..., vocab_size] + + Outputs: + if `masked_lm_labels` is not `None`: + Outputs the masked language modeling loss. + if `masked_lm_labels` is `None`: + Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = BertForMaskedLM(config) + masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertForMaskedLM, self).__init__(config) + self.bert = BertModel(config) + self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + masked_lm_labels=None, + checkpoint_activations=False): + sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, + output_all_encoded_layers=False) + prediction_scores = self.cls(sequence_output) + + if masked_lm_labels is not None: + loss_fct = CrossEntropyLoss(ignore_index=-1) + masked_lm_loss = loss_fct(prediction_scores.view(-1, + self.config.vocab_size), + masked_lm_labels.view(-1)) + return masked_lm_loss + else: + return prediction_scores + + +class BertForNextSentencePrediction(BertPreTrainedModel): + """BERT model with next sentence prediction head. + This module comprises the BERT model followed by the next sentence classification head. + + Params: + config: a BertConfig class instance with the configuration to build a new model. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size] + with indices selected in [0, 1]. + 0 => next sentence is the continuation, 1 => next sentence is a random sentence. + + Outputs: + if `next_sentence_label` is not `None`: + Outputs the total_loss which is the sum of the masked language modeling loss and the next + sentence classification loss. + if `next_sentence_label` is `None`: + Outputs the next sentence classification logits of shape [batch_size, 2]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = BertForNextSentencePrediction(config) + seq_relationship_logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertForNextSentencePrediction, self).__init__(config) + self.bert = BertModel(config) + self.cls = BertOnlyNSPHead(config) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + next_sentence_label=None, + checkpoint_activations=False): + _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, + output_all_encoded_layers=False) + seq_relationship_score = self.cls(pooled_output) + + if next_sentence_label is not None: + loss_fct = CrossEntropyLoss(ignore_index=-1) + next_sentence_loss = loss_fct(seq_relationship_score.view(-1, + 2), + next_sentence_label.view(-1)) + return next_sentence_loss + else: + return seq_relationship_score + + +class BertForSequenceClassification(BertPreTrainedModel): + """BERT model for classification. + This module is composed of the BERT model with a linear layer on top of + the pooled output. + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + `num_labels`: the number of classes for the classifier. Default = 2. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `labels`: labels for the classification output: torch.LongTensor of shape [batch_size] + with indices selected in [0, ..., num_labels]. + + Outputs: + if `labels` is not `None`: + Outputs the CrossEntropy classification loss of the output with the labels. + if `labels` is `None`: + Outputs the classification logits of shape [batch_size, num_labels]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + num_labels = 2 + + model = BertForSequenceClassification(config, num_labels) + logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, num_labels): + super(BertForSequenceClassification, self).__init__(config) + self.num_labels = num_labels + self.bert = BertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, num_labels) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + labels=None, + checkpoint_activations=False): + _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + return loss + else: + return logits + + +class BertForMultipleChoice(BertPreTrainedModel): + """BERT model for multiple choice tasks. + This module is composed of the BERT model with a linear layer on top of + the pooled output. + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + `num_choices`: the number of classes for the classifier. Default = 2. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] + with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` + and type 1 corresponds to a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `labels`: labels for the classification output: torch.LongTensor of shape [batch_size] + with indices selected in [0, ..., num_choices]. + + Outputs: + if `labels` is not `None`: + Outputs the CrossEntropy classification loss of the output with the labels. + if `labels` is `None`: + Outputs the classification logits of shape [batch_size, num_labels]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]]) + input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]]) + token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]]) + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + num_choices = 2 + + model = BertForMultipleChoice(config, num_choices) + logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, num_choices): + super(BertForMultipleChoice, self).__init__(config) + self.num_choices = num_choices + self.bert = BertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, 1) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + labels=None, + checkpoint_activations=False): + flat_input_ids = input_ids.view(-1, input_ids.size(-1)) + flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) + flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) + _, pooled_output = self.bert(flat_input_ids, flat_token_type_ids, flat_attention_mask, output_all_encoded_layers=False) + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, self.num_choices) + + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + return loss + else: + return reshaped_logits + + +class BertForTokenClassification(BertPreTrainedModel): + """BERT model for token-level classification. + This module is composed of the BERT model with a linear layer on top of + the full hidden state of the last layer. + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + `num_labels`: the number of classes for the classifier. Default = 2. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `labels`: labels for the classification output: torch.LongTensor of shape [batch_size, sequence_length] + with indices selected in [0, ..., num_labels]. + + Outputs: + if `labels` is not `None`: + Outputs the CrossEntropy classification loss of the output with the labels. + if `labels` is `None`: + Outputs the classification logits of shape [batch_size, sequence_length, num_labels]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + num_labels = 2 + + model = BertForTokenClassification(config, num_labels) + logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, num_labels): + super(BertForTokenClassification, self).__init__(config) + self.num_labels = num_labels + self.bert = BertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, num_labels) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + labels=None, + checkpoint_activations=False): + sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + if labels is not None: + loss_fct = CrossEntropyLoss() + # Only keep active parts of the loss + if attention_mask is not None: + active_loss = attention_mask.view(-1) == 1 + active_logits = logits.view(-1, self.num_labels)[active_loss] + active_labels = labels.view(-1)[active_loss] + loss = loss_fct(active_logits, active_labels) + else: + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + return loss + else: + return logits + + +class BertForQuestionAnswering(BertPreTrainedModel): + """BERT model for Question Answering (span extraction). + This module is composed of the BERT model with a linear layer on top of + the sequence output that computes start_logits and end_logits + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size]. + Positions are clamped to the length of the sequence and position outside of the sequence are not taken + into account for computing the loss. + `end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size]. + Positions are clamped to the length of the sequence and position outside of the sequence are not taken + into account for computing the loss. + + Outputs: + if `start_positions` and `end_positions` are not `None`: + Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions. + if `start_positions` or `end_positions` is `None`: + Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end + position tokens of shape [batch_size, sequence_length]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = BertForQuestionAnswering(config) + start_logits, end_logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertForQuestionAnswering, self).__init__(config) + self.bert = BertModel(config) + # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version + # self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.qa_outputs = nn.Linear(config.hidden_size, 2) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + start_positions=None, + end_positions=None, + checkpoint_activations=False): + sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1) + end_logits = end_logits.squeeze(-1) + + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions.clamp_(0, ignored_index) + end_positions.clamp_(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + return total_loss + else: + return start_logits, end_logits diff --git a/tests/unit/modelingpreln.py b/tests/unit/modelingpreln.py new file mode 100755 index 000000000000..e9e00727353f --- /dev/null +++ b/tests/unit/modelingpreln.py @@ -0,0 +1,1665 @@ +# DeepSpeed note, code taken from commit 3d59216cec89a363649b4fe3d15295ba936ced0f +# https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/modeling.py + +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch BERT model.""" + +from __future__ import absolute_import, division, print_function, unicode_literals + +import copy +import json +import logging +import math +import os +import shutil +import tarfile +import tempfile +import sys +from io import open + +import torch +from torch import nn +from torch.nn import CrossEntropyLoss +from torch.utils import checkpoint +import torch.distributed as dist + +from torch.nn import Module +from torch.nn.parameter import Parameter +import torch.nn.functional as F +import torch.nn.init as init +import time + +#from numba import cuda + +#from deepspeed_cuda import DeepSpeedSoftmaxConfig, DeepSpeedSoftmax + +logger = logging.getLogger(__name__) + +PRETRAINED_MODEL_ARCHIVE_MAP = { + 'bert-base-uncased': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz", + 'bert-large-uncased': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz", + 'bert-base-cased': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz", + 'bert-large-cased': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz", + 'bert-base-multilingual-uncased': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz", + 'bert-base-multilingual-cased': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz", + 'bert-base-chinese': + "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz", +} +CONFIG_NAME = 'bert_config.json' +WEIGHTS_NAME = 'pytorch_model.bin' +TF_WEIGHTS_NAME = 'model.ckpt' + + +def load_tf_weights_in_bert(model, tf_checkpoint_path): + """ Load tf checkpoints in a pytorch model + """ + try: + import re + import numpy as np + import tensorflow as tf + except ImportError: + print( + "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see " + "https://www.tensorflow.org/install/ for installation instructions.") + raise + tf_path = os.path.abspath(tf_checkpoint_path) + print("Converting TensorFlow checkpoint from {}".format(tf_path)) + # Load weights from TF model + init_vars = tf.train.list_variables(tf_path) + names = [] + arrays = [] + for name, shape in init_vars: + print("Loading TF weight {} with shape {}".format(name, shape)) + array = tf.train.load_variable(tf_path, name) + names.append(name) + arrays.append(array) + + for name, array in zip(names, arrays): + name = name.split('/') + # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v + # which are not required for using pretrained model + if any(n in ["adam_v", "adam_m"] for n in name): + print("Skipping {}".format("/".join(name))) + continue + pointer = model + for m_name in name: + if re.fullmatch(r'[A-Za-z]+_\d+', m_name): + l = re.split(r'_(\d+)', m_name) + else: + l = [m_name] + if l[0] == 'kernel' or l[0] == 'gamma': + pointer = getattr(pointer, 'weight') + elif l[0] == 'output_bias' or l[0] == 'beta': + pointer = getattr(pointer, 'bias') + elif l[0] == 'output_weights': + pointer = getattr(pointer, 'weight') + else: + pointer = getattr(pointer, l[0]) + if len(l) >= 2: + num = int(l[1]) + pointer = pointer[num] + if m_name[-11:] == '_embeddings': + pointer = getattr(pointer, 'weight') + elif m_name == 'kernel': + array = np.transpose(array) + try: + assert pointer.shape == array.shape + except AssertionError as e: + e.args += (pointer.shape, array.shape) + raise + print("Initialize PyTorch weight {}".format(name)) + pointer.data = torch.from_numpy(array) + return model + + +@torch.jit.script +def f_gelu(x): + return x * 0.5 * (1.0 + torch.erf(x / 1.41421)) + + +@torch.jit.script +def bias_gelu(bias, y): + x = bias + y + return x * 0.5 * (1.0 + torch.erf(x / 1.41421)) + + +@torch.jit.script +def bias_tanh(bias, y): + x = bias + y + return torch.tanh(x) + + +def gelu(x): + """Implementation of the gelu activation function. + For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): + 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) + Also see https://arxiv.org/abs/1606.08415 + """ + return f_gelu(x) + + +def swish(x): + return x * torch.sigmoid(x) + + +ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish} + + +class GPUTimer: + def __init__(self): + super().__init__() + self.start = cuda.event() + self.stop = cuda.event() + + def record(self): + self.start.record() + + def elapsed(self): + self.stop.record() + self.stop.synchronize() + return self.start.elapsed_time(self.stop) / 1000.0 + + +class LinearActivation(Module): + r"""Fused Linear and activation Module. + """ + __constants__ = ['bias'] + + def __init__(self, + in_features, + out_features, + weights, + biases, + act='gelu', + bias=True): + super(LinearActivation, self).__init__() + self.in_features = in_features + self.out_features = out_features + self.fused_gelu = False + self.fused_tanh = False + if isinstance(act, + str) or (sys.version_info[0] == 2 and isinstance(act, + unicode)): + if bias and act == 'gelu': + self.fused_gelu = True + elif bias and act == 'tanh': + self.fused_tanh = True + else: + self.act_fn = ACT2FN[act] + else: + self.act_fn = act + #self.weight = Parameter(torch.Tensor(out_features, in_features)) + self.weight = weights[5] + self.bias = biases[5] + #if bias: + # self.bias = Parameter(torch.Tensor(out_features)) + #else: + # self.register_parameter('bias', None) + #self.reset_parameters() + + def reset_parameters(self): + init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + if self.bias is not None: + fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight) + bound = 1 / math.sqrt(fan_in) + init.uniform_(self.bias, -bound, bound) + + def forward(self, input): + if self.fused_gelu: + #timing = [] + #t1 = GPUTimer() + #t1.record() + y = F.linear(input, self.weight, None) + #timing.append(t1.elapsed()) + #t1.record() + bg = bias_gelu(self.bias, y) + #timing.append(t1.elapsed()) + return bg + elif self.fused_tanh: + return bias_tanh(self.bias, F.linear(input, self.weight, None)) + else: + return self.act_fn(F.linear(input, self.weight, self.bias)) + + def extra_repr(self): + return 'in_features={}, out_features={}, bias={}'.format( + self.in_features, + self.out_features, + self.bias is not None) + + +class BertConfig(object): + """Configuration class to store the configuration of a `BertModel`. + """ + def __init__(self, + vocab_size_or_config_json_file, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + batch_size=8, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=512, + type_vocab_size=2, + initializer_range=0.02, + fp16=False): + """Constructs BertConfig. + + Args: + vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`. + hidden_size: Size of the encoder layers and the pooler layer. + num_hidden_layers: Number of hidden layers in the Transformer encoder. + num_attention_heads: Number of attention heads for each attention layer in + the Transformer encoder. + intermediate_size: The size of the "intermediate" (i.e., feed-forward) + layer in the Transformer encoder. + hidden_act: The non-linear activation function (function or string) in the + encoder and pooler. If string, "gelu", "relu" and "swish" are supported. + hidden_dropout_prob: The dropout probabilitiy for all fully connected + layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob: The dropout ratio for the attention + probabilities. + max_position_embeddings: The maximum sequence length that this model might + ever be used with. Typically set this to something large just in case + (e.g., 512 or 1024 or 2048). + type_vocab_size: The vocabulary size of the `token_type_ids` passed into + `BertModel`. + initializer_range: The sttdev of the truncated_normal_initializer for + initializing all weight matrices. + """ + if isinstance(vocab_size_or_config_json_file, + str) or (sys.version_info[0] == 2 + and isinstance(vocab_size_or_config_json_file, + unicode)): + with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader: + json_config = json.loads(reader.read()) + for key, value in json_config.items(): + self.__dict__[key] = value + elif isinstance(vocab_size_or_config_json_file, int): + self.vocab_size = vocab_size_or_config_json_file + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.batch_size = batch_size + self.hidden_act = hidden_act + self.intermediate_size = intermediate_size + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.type_vocab_size = type_vocab_size + self.initializer_range = initializer_range + self.fp16 = fp16 + else: + raise ValueError("First argument must be either a vocabulary size (int)" + "or the path to a pretrained model config file (str)") + + @classmethod + def from_dict(cls, json_object): + """Constructs a `BertConfig` from a Python dictionary of parameters.""" + config = BertConfig(vocab_size_or_config_json_file=-1) + for key, value in json_object.items(): + config.__dict__[key] = value + return config + + @classmethod + def from_json_file(cls, json_file): + """Constructs a `BertConfig` from a json file of parameters.""" + with open(json_file, "r", encoding='utf-8') as reader: + text = reader.read() + return cls.from_dict(json.loads(text)) + + def __repr__(self): + return str(self.to_json_string()) + + def to_dict(self): + """Serializes this instance to a Python dictionary.""" + output = copy.deepcopy(self.__dict__) + return output + + def to_json_string(self): + """Serializes this instance to a JSON string.""" + return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" + + +try: + import apex + #apex.amp.register_half_function(apex.normalization.fused_layer_norm, 'FusedLayerNorm') + import apex.normalization + #apex.amp.register_float_function(apex.normalization.FusedLayerNorm, 'forward') + BertLayerNorm = apex.normalization.FusedLayerNorm +except ImportError: + print( + "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex." + ) + + class BertLayerNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-12): + """Construct a layernorm module in the TF style (epsilon inside the square root). + """ + super(BertLayerNorm, self).__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.bias = nn.Parameter(torch.zeros(hidden_size)) + self.variance_epsilon = eps + + def forward(self, x): + u = x.mean(-1, keepdim=True) + s = (x - u).pow(2).mean(-1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.variance_epsilon) + return self.weight * x + self.bias + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings. + """ + def __init__(self, config): + super(BertEmbeddings, self).__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, + config.hidden_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, + config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, input_ids, token_type_ids=None): + seq_length = input_ids.size(1) + position_ids = torch.arange(seq_length, + dtype=torch.long, + device=input_ids.device) + position_ids = position_ids.unsqueeze(0).expand_as(input_ids) + if token_type_ids is None: + token_type_ids = torch.zeros_like(input_ids) + + words_embeddings = self.word_embeddings(input_ids) + position_embeddings = self.position_embeddings(position_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = words_embeddings + position_embeddings + token_type_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertSelfAttention(nn.Module): + def __init__(self, i, config, weights, biases): + super(BertSelfAttention, self).__init__() + if config.hidden_size % config.num_attention_heads != 0: + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, + config.num_attention_heads)) + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.query.weight = weights[0] + self.query.bias = biases[0] + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.key.weight = weights[1] + self.key.bias = biases[1] + self.value = nn.Linear(config.hidden_size, self.all_head_size) + self.value.weight = weights[2] + self.value.bias = biases[2] + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.softmax = nn.Softmax(dim=-1) + #self.softmax_config = DeepSpeedSoftmaxConfig() + #self.softmax_config.batch_size = config.batch_size + #self.softmax_config.max_seq_length = config.max_position_embeddings + #self.softmax_config.hidden_size = config.hidden_size + #self.softmax_config.heads = config.num_attention_heads + #self.softmax_config.softmax_id = i + #self.softmax_config.fp16 = config.fp16 + #self.softmax_config.prob_drop_out = 0.0 + #self.softmax = DeepSpeedSoftmax(i, self.softmax_config) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, + self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def transpose_key_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, + self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 3, 1) + + def forward(self, hidden_states, attention_mask, grads=None): + #timing = [] + #t1 = GPUTimer() + #t1.record() + mixed_query_layer = self.query(hidden_states) + + #timing.append(t1.elapsed()) + #print("Query elapsed: %s" % (time.clock() - start)) + #t1.record() + mixed_key_layer = self.key(hidden_states) + + #timing.append(t1.elapsed()) + #print("Key elapsed: %s" % (time.clock() - start)) + #t1.record() + mixed_value_layer = self.value(hidden_states) + #timing.append(t1.elapsed()) + #print("Value elapsed: %s" % (time.clock() - start)) + + #t1.record() + query_layer = self.transpose_for_scores(mixed_query_layer) + # print(query_layer) + #timing.append(t1.elapsed()) + #print("Query-Transform elapsed: %s" % (time.clock() - start)) + #t1.record() + key_layer = self.transpose_key_for_scores(mixed_key_layer) + # print(key_layer) + #timing.append(t1.elapsed()) + #print("Key-Transform elapsed: %s" % (time.clock() - start)) + #t1.record() + value_layer = self.transpose_for_scores(mixed_value_layer) + #print(value_layer) + #timing.append(t1.elapsed()) + #print("Value-Transform elapsed: %s" % (time.clock() - start)) + + # Take the dot product between "query" and "key" to get the raw attention scores. + #t1.record() + #print(query_layer.shape) + #print(key_layer.shape) + attention_scores = torch.matmul(query_layer, key_layer) + #print(attention_scores.shape) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + #print("Pytorch: ", attention_scores) + #timing.append(t1.elapsed()) + #print("Attention-Score elapsed: %s" % (time.clock() - start)) + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + #t1.record() + + # context_layer = self.softmax(query_layer, key_layer, value_layer, attention_mask) + #print("context shape is :", context_layer.shape) + #print("Cuda-ext:, ", attention_scores1) + # Normalize the attention scores to probabilities. + ####attention_probs = self.softmax(attention_scores) + #timing.append(t1.elapsed()) + #print("Softmax elapsed: %s" % (time.clock() - start)) + #t1 = GPUTimer() + #t1.record() + attention_scores = attention_scores + attention_mask + attention_probs = self.softmax(attention_scores) + #attention_scores = self.softmax(attention_scores, attention_mask) + #print("Softmax elapse {0:8.2f} ms", t1.elapsed() * 1000) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + #t1.record() + context_layer = torch.matmul(attention_probs, value_layer) + #timing.append(t1.elapsed()) + #print("Context elapsed: %s" % (time.clock() - start)) + #t1.record() + #context_layer1 = context_layer.permute( + # 0, 1, 3, 2, 4).contiguous() + #if grads is not None: + # context_layer.register_hook(lambda x, self = self : grads.append([x, "Context"])) + context_layer1 = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer1.size()[:-2] + (self.all_head_size, ) + context_layer1 = context_layer1.view(*new_context_layer_shape) + #timing.append(t1.elapsed()) + #print("Context-Transform elapsed: %s" % (time.clock() - start)) + + if grads is not None: + query_layer.register_hook(lambda x, self=self: grads.append([x, "Query"])) + key_layer.register_hook(lambda x, self=self: grads.append([x, "Key"])) + value_layer.register_hook(lambda x, self=self: grads.append([x, "Value"])) + + return context_layer1 + + +class BertSelfOutput(nn.Module): + def __init__(self, config, weights, biases): + super(BertSelfOutput, self).__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dense.weight = weights[3] + self.dense.bias = biases[3] + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + #timing = [] + #t1 = GPUTimer() + #t1.record() + hidden_states = self.dense(hidden_states) + #timing.append(t1.elapsed()) + #print("Attention Output elapsed: %s" % (time.clock() - start)) + hidden_states = self.dropout(hidden_states) + #t1.record() + #hidden_states = self.LayerNorm(hidden_states + input_tensor) + #timing.append(t1.elapsed()) + #print("LayerNorm elapsed: %s" % (time.clock() - start)) + return hidden_states + + def get_w(self): + return self.dense.weight + + +class BertAttention(nn.Module): + def __init__(self, i, config, weights, biases): + super(BertAttention, self).__init__() + self.self = BertSelfAttention(i, config, weights, biases) + self.output = BertSelfOutput(config, weights, biases) + + def forward(self, input_tensor, attention_mask): + self_output = self.self(input_tensor, attention_mask) + attention_output = self.output(self_output, input_tensor) + return attention_output + + def get_w(self): + return self.output.get_w() + + +class BertIntermediate(nn.Module): + def __init__(self, config, weights, biases): + super(BertIntermediate, self).__init__() + self.dense_act = LinearActivation(config.hidden_size, + config.intermediate_size, + weights, + biases, + act=config.hidden_act) + + def forward(self, hidden_states): + hidden_states = self.dense_act(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config, weights, biases): + super(BertOutput, self).__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dense.weight = weights[6] + self.dense.bias = biases[6] + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + #timing = [] + #t1 = GPUTimer() + #t1.record() + #print (hidden_states) + #print (self.dense.weight) + hidden_states = self.dense(hidden_states) + #timing.append(t1.elapsed()) + #print("FF2 elapsed: %s" % (time.clock() - start)) + hidden_states = self.dropout(hidden_states) + #t1.record() + #hidden_states = self.LayerNorm(hidden_states + input_tensor) + #timing.append(t1.elapsed()) + #print("LayerNorm elapsed: %s" % (time.clock() - start)) + return hidden_states + + +class BertLayer(nn.Module): + def __init__(self, i, config, weights, biases): + super(BertLayer, self).__init__() + self.attention = BertAttention(i, config, weights, biases) + self.PreAttentionLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.PostAttentionLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.intermediate = BertIntermediate(config, weights, biases) + self.output = BertOutput(config, weights, biases) + self.weight = weights + self.biases = biases + + def forward(self, hidden_states, attention_mask, grads, collect_all_grads=False): + input_layer_norm = self.PreAttentionLayerNorm(hidden_states) + attention_output = self.attention(input_layer_norm, attention_mask) + #print ("hidden shape is :", hidden_states.shape) + intermediate_input = hidden_states + attention_output + + intermediate_layer_norm = self.PostAttentionLayerNorm(intermediate_input) + intermediate_output = self.intermediate(intermediate_layer_norm) + layer_output = self.output(intermediate_output, attention_output) + + #attention_output = self.attention(hidden_states, attention_mask) + #intermediate_output = self.intermediate(attention_output) + #layer_output = self.output(intermediate_output, attention_output) + + if collect_all_grads: + # self.weight[0].register_hook(lambda x, self=self: grads.append([x,"Q_W"])) + # self.biases[0].register_hook(lambda x, self=self: grads.append([x,"Q_B"])) + # self.weight[1].register_hook(lambda x, self=self: grads.append([x,"K_W"])) + # self.biases[1].register_hook(lambda x, self=self: grads.append([x,"K_B"])) + self.weight[2].register_hook(lambda x, self=self: grads.append([x, "V_W"])) + self.biases[2].register_hook(lambda x, self=self: grads.append([x, "V_B"])) + self.weight[3].register_hook(lambda x, self=self: grads.append([x, "O_W"])) + self.biases[3].register_hook(lambda x, self=self: grads.append([x, "O_B"])) + self.PostAttentionLayerNorm.weight.register_hook( + lambda x, + self=self: grads.append([x, + "N2_W"])) + self.PostAttentionLayerNorm.bias.register_hook( + lambda x, + self=self: grads.append([x, + "N2_B"])) + self.weight[5].register_hook(lambda x, self=self: grads.append([x, "int_W"])) + self.biases[5].register_hook(lambda x, self=self: grads.append([x, "int_B"])) + self.weight[6].register_hook(lambda x, self=self: grads.append([x, "out_W"])) + self.biases[6].register_hook(lambda x, self=self: grads.append([x, "out_B"])) + self.PreAttentionLayerNorm.weight.register_hook( + lambda x, + self=self: grads.append([x, + "norm_W"])) + self.PreAttentionLayerNorm.bias.register_hook( + lambda x, + self=self: grads.append([x, + "norm_B"])) + + return layer_output + intermediate_input + + def get_w(self): + return self.attention.get_w() + + +class BertEncoder(nn.Module): + def __init__(self, config, weights, biases): + super(BertEncoder, self).__init__() + #layer = BertLayer(config, weights, biases) + self.FinalLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + + self.layer = nn.ModuleList([ + copy.deepcopy(BertLayer(i, + config, + weights, + biases)) for i in range(config.num_hidden_layers) + ]) + self.grads = [] + self.graph = [] + + def get_grads(self): + return self.grads + + # def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True): + # all_encoder_layers = [] + # for layer_module in self.layer: + # hidden_states = layer_module(hidden_states, attention_mask) + # if output_all_encoded_layers: + # all_encoder_layers.append(hidden_states) + # if not output_all_encoded_layers: + # all_encoder_layers.append(hidden_states) + # return all_encoder_layers + + def get_modules(self, big_node, input): + for mdl in big_node.named_children(): + graph.append(mdl) + get_modules(self, mdl, input) + + def forward(self, + hidden_states, + attention_mask, + output_all_encoded_layers=True, + checkpoint_activations=False): + all_encoder_layers = [] + + def custom(start, end): + def custom_forward(*inputs): + layers = self.layer[start:end] + x_ = inputs[0] + for layer in layers: + x_ = layer(x_, inputs[1]) + return x_ + + return custom_forward + + if checkpoint_activations: + l = 0 + num_layers = len(self.layer) + chunk_length = math.ceil(math.sqrt(num_layers)) + while l < num_layers: + hidden_states = checkpoint.checkpoint(custom(l, + l + chunk_length), + hidden_states, + attention_mask * 1) + l += chunk_length + # decoder layers + else: + for i, layer_module in enumerate(self.layer): + hidden_states = layer_module(hidden_states, + attention_mask, + self.grads, + collect_all_grads=True) + hidden_states.register_hook( + lambda x, + i=i, + self=self: self.grads.append([x, + "hidden_state"])) + #print("pytorch weight is: ", layer_module.get_w()) + + if output_all_encoded_layers: + all_encoder_layers.append((hidden_states)) + + if not output_all_encoded_layers or checkpoint_activations: + hidden_states = self.FinalLayerNorm(hidden_states) + all_encoder_layers.append((hidden_states)) + return all_encoder_layers + + +#class BertEncoder(nn.Module): +# def __init__(self, config): +# super(BertEncoder, self).__init__() +# layer = BertLayer(config) +# self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]) +# +# def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True): +# all_encoder_layers = [] +# for layer_module in self.layer: +# hidden_states = layer_module(hidden_states, attention_mask) +# if output_all_encoded_layers: +# all_encoder_layers.append(hidden_states) +# if not output_all_encoded_layers: +# all_encoder_layers.append(hidden_states) +# return all_encoder_layers + + +class BertPooler(nn.Module): + def __init__(self, config): + super(BertPooler, self).__init__() + self.dense_act = LinearActivation(config.hidden_size, + config.hidden_size, + act="tanh") + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense_act(first_token_tensor) + return pooled_output + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super(BertPredictionHeadTransform, self).__init__() + self.dense_act = LinearActivation(config.hidden_size, + config.hidden_size, + act=config.hidden_act) + self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + + def forward(self, hidden_states): + hidden_states = self.dense_act(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config, bert_model_embedding_weights): + super(BertLMPredictionHead, self).__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(bert_model_embedding_weights.size(1), + bert_model_embedding_weights.size(0), + bias=False) + self.decoder.weight = bert_model_embedding_weights + self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0))) + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + torch.cuda.nvtx.range_push( + "decoder input.size() = {}, weight.size() = {}".format( + hidden_states.size(), + self.decoder.weight.size())) + hidden_states = self.decoder(hidden_states) + self.bias + torch.cuda.nvtx.range_pop() + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config, bert_model_embedding_weights): + super(BertOnlyMLMHead, self).__init__() + self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class BertOnlyNSPHead(nn.Module): + def __init__(self, config): + super(BertOnlyNSPHead, self).__init__() + self.seq_relationship = nn.Linear(config.hidden_size, 2) + + def forward(self, pooled_output): + seq_relationship_score = self.seq_relationship(pooled_output) + return seq_relationship_score + + +class BertPreTrainingHeads(nn.Module): + def __init__(self, config, bert_model_embedding_weights): + super(BertPreTrainingHeads, self).__init__() + self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights) + self.seq_relationship = nn.Linear(config.hidden_size, 2) + + def forward(self, sequence_output, pooled_output): + prediction_scores = self.predictions(sequence_output) + seq_relationship_score = self.seq_relationship(pooled_output) + return prediction_scores, seq_relationship_score + + +class BertPreTrainedModel(nn.Module): + """ An abstract class to handle weights initialization and + a simple interface for dowloading and loading pretrained models. + """ + def __init__(self, config, *inputs, **kwargs): + super(BertPreTrainedModel, self).__init__() + if not isinstance(config, BertConfig): + raise ValueError( + "Parameter config in `{}(config)` should be an instance of class `BertConfig`. " + "To create a model from a Google pretrained model use " + "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format( + self.__class__.__name__, + self.__class__.__name__)) + self.config = config + + def init_bert_weights(self, module): + """ Initialize the weights. + """ + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, BertLayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + @classmethod + def from_pretrained(cls, + pretrained_model_name_or_path, + state_dict=None, + cache_dir=None, + from_tf=False, + *inputs, + **kwargs): + """ + Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict. + Download and cache the pre-trained model file if needed. + + Params: + pretrained_model_name_or_path: either: + - a str with the name of a pre-trained model to load selected in the list of: + . `bert-base-uncased` + . `bert-large-uncased` + . `bert-base-cased` + . `bert-large-cased` + . `bert-base-multilingual-uncased` + . `bert-base-multilingual-cased` + . `bert-base-chinese` + - a path or url to a pretrained model archive containing: + . `bert_config.json` a configuration file for the model + . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance + - a path or url to a pretrained model archive containing: + . `bert_config.json` a configuration file for the model + . `model.chkpt` a TensorFlow checkpoint + from_tf: should we load the weights from a locally saved TensorFlow checkpoint + cache_dir: an optional path to a folder in which the pre-trained models will be cached. + state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models + *inputs, **kwargs: additional input for the specific Bert class + (ex: num_labels for BertForSequenceClassification) + """ + if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP: + archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path] + else: + archive_file = pretrained_model_name_or_path + if resolved_archive_file == archive_file: + logger.info("loading archive file {}".format(archive_file)) + else: + logger.info("loading archive file {} from cache at {}".format( + archive_file, + resolved_archive_file)) + tempdir = None + if os.path.isdir(resolved_archive_file) or from_tf: + serialization_dir = resolved_archive_file + else: + # Extract archive to temp dir + tempdir = tempfile.mkdtemp() + logger.info("extracting archive file {} to temp dir {}".format( + resolved_archive_file, + tempdir)) + with tarfile.open(resolved_archive_file, 'r:gz') as archive: + archive.extractall(tempdir) + serialization_dir = tempdir + # Load config + config_file = os.path.join(serialization_dir, CONFIG_NAME) + config = BertConfig.from_json_file(config_file) + logger.info("Model config {}".format(config)) + # Instantiate model. + model = cls(config, *inputs, **kwargs) + if state_dict is None and not from_tf: + weights_path = os.path.join(serialization_dir, WEIGHTS_NAME) + state_dict = torch.load( + weights_path, + map_location='cpu' if not torch.cuda.is_available() else None) + if tempdir: + # Clean up temp dir + shutil.rmtree(tempdir) + if from_tf: + # Directly load from a TensorFlow checkpoint + weights_path = os.path.join(serialization_dir, TF_WEIGHTS_NAME) + return load_tf_weights_in_bert(model, weights_path) + # Load from a PyTorch state_dict + old_keys = [] + new_keys = [] + for key in state_dict.keys(): + new_key = None + if 'gamma' in key: + new_key = key.replace('gamma', 'weight') + if 'beta' in key: + new_key = key.replace('beta', 'bias') + if new_key: + old_keys.append(key) + new_keys.append(new_key) + for old_key, new_key in zip(old_keys, new_keys): + state_dict[new_key] = state_dict.pop(old_key) + + missing_keys = [] + unexpected_keys = [] + error_msgs = [] + # copy state_dict so _load_from_state_dict can modify it + metadata = getattr(state_dict, '_metadata', None) + state_dict = state_dict.copy() + if metadata is not None: + state_dict._metadata = metadata + + def load(module, prefix=''): + local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) + module._load_from_state_dict(state_dict, + prefix, + local_metadata, + True, + missing_keys, + unexpected_keys, + error_msgs) + for name, child in module._modules.items(): + if child is not None: + load(child, prefix + name + '.') + + start_prefix = '' + if not hasattr(model, + 'bert') and any(s.startswith('bert.') for s in state_dict.keys()): + start_prefix = 'bert.' + load(model, prefix=start_prefix) + if len(missing_keys) > 0: + logger.info("Weights of {} not initialized from pretrained model: {}".format( + model.__class__.__name__, + missing_keys)) + if len(unexpected_keys) > 0: + logger.info("Weights from pretrained model not used in {}: {}".format( + model.__class__.__name__, + unexpected_keys)) + if len(error_msgs) > 0: + raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( + model.__class__.__name__, + "\n\t".join(error_msgs))) + return model + + +class BertModel(BertPreTrainedModel): + """BERT model ("Bidirectional Embedding Representations from a Transformer"). + + Params: + config: a BertConfig class instance with the configuration to build a new model + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`. + + Outputs: Tuple of (encoded_layers, pooled_output) + `encoded_layers`: controled by `output_all_encoded_layers` argument: + - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end + of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each + encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size], + - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding + to the last attention block of shape [batch_size, sequence_length, hidden_size], + `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a + classifier pretrained on top of the hidden state associated to the first character of the + input (`CLS`) to train on the Next-Sentence task (see BERT's paper). + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = modeling.BertModel(config=config) + all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertModel, self).__init__(config) + self.embeddings = BertEmbeddings(config) + self.encoder = BertEncoder(config) + self.pooler = BertPooler(config) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + output_all_encoded_layers=True, + checkpoint_activations=False): + if attention_mask is None: + attention_mask = torch.ones_like(input_ids) + if token_type_ids is None: + token_type_ids = torch.zeros_like(input_ids) + + # We create a 3D attention mask from a 2D tensor mask. + # Sizes are [batch_size, 1, 1, to_seq_length] + # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] + # this attention mask is more simple than the triangular masking of causal attention + # used in OpenAI GPT, we just need to prepare the broadcast dimension here. + extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to(dtype=next( + self.parameters()).dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + + embedding_output = self.embeddings(input_ids, token_type_ids) + encoded_layers = self.encoder( + embedding_output, + extended_attention_mask, + output_all_encoded_layers=output_all_encoded_layers, + checkpoint_activations=checkpoint_activations) + sequence_output = encoded_layers[-1] + pooled_output = self.pooler(sequence_output) + if not output_all_encoded_layers: + encoded_layers = encoded_layers[-1] + return encoded_layers, pooled_output + + +class BertForPreTraining(BertPreTrainedModel): + """BERT model with pre-training heads. + This module comprises the BERT model followed by the two pre-training heads: + - the masked language modeling head, and + - the next sentence classification head. + + Params: + config: a BertConfig class instance with the configuration to build a new model. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `masked_lm_labels`: optional masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] + with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss + is only computed for the labels set in [0, ..., vocab_size] + `next_sentence_label`: optional next sentence classification loss: torch.LongTensor of shape [batch_size] + with indices selected in [0, 1]. + 0 => next sentence is the continuation, 1 => next sentence is a random sentence. + + Outputs: + if `masked_lm_labels` and `next_sentence_label` are not `None`: + Outputs the total_loss which is the sum of the masked language modeling loss and the next + sentence classification loss. + if `masked_lm_labels` or `next_sentence_label` is `None`: + Outputs a tuple comprising + - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and + - the next sentence classification logits of shape [batch_size, 2]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = BertForPreTraining(config) + masked_lm_logits_scores, seq_relationship_logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, args): + super(BertForPreTraining, self).__init__(config) + self.summary_writer = None + if dist.get_rank() == 0: + self.summary_writer = args.summary_writer + self.samples_per_step = dist.get_world_size() * args.train_batch_size + self.sample_count = self.samples_per_step + self.bert = BertModel(config) + self.cls = BertPreTrainingHeads(config, + self.bert.embeddings.word_embeddings.weight) + self.apply(self.init_bert_weights) + + def log_summary_writer(self, logs: dict, base='Train'): + if dist.get_rank() == 0: + module_name = "Samples" #self._batch_module_name.get(batch_type, self._get_batch_type_error(batch_type)) + for key, log in logs.items(): + self.summary_writer.add_scalar(f'{base}/{module_name}/{key}', + log, + self.sample_count) + self.sample_count += self.samples_per_step + + def forward(self, batch, log=True): + #input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, next_sentence_label=None, checkpoint_activations=False): + input_ids = batch[1] + token_type_ids = batch[3] + attention_mask = batch[2] + masked_lm_labels = batch[5] + next_sentence_label = batch[4] + checkpoint_activations = False + + sequence_output, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, + output_all_encoded_layers=False, checkpoint_activations=checkpoint_activations) + prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) + + if masked_lm_labels is not None and next_sentence_label is not None: + loss_fct = CrossEntropyLoss(ignore_index=-1) + masked_lm_loss = loss_fct(prediction_scores.view(-1, + self.config.vocab_size), + masked_lm_labels.view(-1)) + next_sentence_loss = loss_fct(seq_relationship_score.view(-1, + 2), + next_sentence_label.view(-1)) + #print("loss is {} {}".format(masked_lm_loss, next_sentence_loss)) + total_loss = masked_lm_loss + next_sentence_loss + # if log: + # self.log_summary_writer(logs={'train_loss': total_loss.item()}) + return total_loss + else: + return prediction_scores, seq_relationship_score + + +class BertForMaskedLM(BertPreTrainedModel): + """BERT model with the masked language modeling head. + This module comprises the BERT model followed by the masked language modeling head. + + Params: + config: a BertConfig class instance with the configuration to build a new model. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length] + with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss + is only computed for the labels set in [0, ..., vocab_size] + + Outputs: + if `masked_lm_labels` is not `None`: + Outputs the masked language modeling loss. + if `masked_lm_labels` is `None`: + Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = BertForMaskedLM(config) + masked_lm_logits_scores = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertForMaskedLM, self).__init__(config) + self.bert = BertModel(config) + self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + masked_lm_labels=None, + checkpoint_activations=False): + sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, + output_all_encoded_layers=False) + prediction_scores = self.cls(sequence_output) + + if masked_lm_labels is not None: + loss_fct = CrossEntropyLoss(ignore_index=-1) + masked_lm_loss = loss_fct(prediction_scores.view(-1, + self.config.vocab_size), + masked_lm_labels.view(-1)) + return masked_lm_loss + else: + return prediction_scores + + +class BertForNextSentencePrediction(BertPreTrainedModel): + """BERT model with next sentence prediction head. + This module comprises the BERT model followed by the next sentence classification head. + + Params: + config: a BertConfig class instance with the configuration to build a new model. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size] + with indices selected in [0, 1]. + 0 => next sentence is the continuation, 1 => next sentence is a random sentence. + + Outputs: + if `next_sentence_label` is not `None`: + Outputs the total_loss which is the sum of the masked language modeling loss and the next + sentence classification loss. + if `next_sentence_label` is `None`: + Outputs the next sentence classification logits of shape [batch_size, 2]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = BertForNextSentencePrediction(config) + seq_relationship_logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertForNextSentencePrediction, self).__init__(config) + self.bert = BertModel(config) + self.cls = BertOnlyNSPHead(config) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + next_sentence_label=None, + checkpoint_activations=False): + _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, + output_all_encoded_layers=False) + seq_relationship_score = self.cls(pooled_output) + + if next_sentence_label is not None: + loss_fct = CrossEntropyLoss(ignore_index=-1) + next_sentence_loss = loss_fct(seq_relationship_score.view(-1, + 2), + next_sentence_label.view(-1)) + return next_sentence_loss + else: + return seq_relationship_score + + +class BertForSequenceClassification(BertPreTrainedModel): + """BERT model for classification. + This module is composed of the BERT model with a linear layer on top of + the pooled output. + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + `num_labels`: the number of classes for the classifier. Default = 2. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `labels`: labels for the classification output: torch.LongTensor of shape [batch_size] + with indices selected in [0, ..., num_labels]. + + Outputs: + if `labels` is not `None`: + Outputs the CrossEntropy classification loss of the output with the labels. + if `labels` is `None`: + Outputs the classification logits of shape [batch_size, num_labels]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + num_labels = 2 + + model = BertForSequenceClassification(config, num_labels) + logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, num_labels): + super(BertForSequenceClassification, self).__init__(config) + self.num_labels = num_labels + self.bert = BertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, num_labels) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + labels=None, + checkpoint_activations=False): + _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + return loss + else: + return logits + + +class BertForMultipleChoice(BertPreTrainedModel): + """BERT model for multiple choice tasks. + This module is composed of the BERT model with a linear layer on top of + the pooled output. + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + `num_choices`: the number of classes for the classifier. Default = 2. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] + with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` + and type 1 corresponds to a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `labels`: labels for the classification output: torch.LongTensor of shape [batch_size] + with indices selected in [0, ..., num_choices]. + + Outputs: + if `labels` is not `None`: + Outputs the CrossEntropy classification loss of the output with the labels. + if `labels` is `None`: + Outputs the classification logits of shape [batch_size, num_labels]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]]) + input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]]) + token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]]) + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + num_choices = 2 + + model = BertForMultipleChoice(config, num_choices) + logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, num_choices): + super(BertForMultipleChoice, self).__init__(config) + self.num_choices = num_choices + self.bert = BertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, 1) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + labels=None, + checkpoint_activations=False): + flat_input_ids = input_ids.view(-1, input_ids.size(-1)) + flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) + flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) + _, pooled_output = self.bert(flat_input_ids, flat_token_type_ids, flat_attention_mask, output_all_encoded_layers=False) + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, self.num_choices) + + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + return loss + else: + return reshaped_logits + + +class BertForTokenClassification(BertPreTrainedModel): + """BERT model for token-level classification. + This module is composed of the BERT model with a linear layer on top of + the full hidden state of the last layer. + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + `num_labels`: the number of classes for the classifier. Default = 2. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `labels`: labels for the classification output: torch.LongTensor of shape [batch_size, sequence_length] + with indices selected in [0, ..., num_labels]. + + Outputs: + if `labels` is not `None`: + Outputs the CrossEntropy classification loss of the output with the labels. + if `labels` is `None`: + Outputs the classification logits of shape [batch_size, sequence_length, num_labels]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + num_labels = 2 + + model = BertForTokenClassification(config, num_labels) + logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config, num_labels): + super(BertForTokenClassification, self).__init__(config) + self.num_labels = num_labels + self.bert = BertModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, num_labels) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + labels=None, + checkpoint_activations=False): + sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + if labels is not None: + loss_fct = CrossEntropyLoss() + # Only keep active parts of the loss + if attention_mask is not None: + active_loss = attention_mask.view(-1) == 1 + active_logits = logits.view(-1, self.num_labels)[active_loss] + active_labels = labels.view(-1)[active_loss] + loss = loss_fct(active_logits, active_labels) + else: + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + return loss + else: + return logits + + +class BertForQuestionAnswering(BertPreTrainedModel): + """BERT model for Question Answering (span extraction). + This module is composed of the BERT model with a linear layer on top of + the sequence output that computes start_logits and end_logits + + Params: + `config`: a BertConfig class instance with the configuration to build a new model. + + Inputs: + `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] + with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts + `extract_features.py`, `run_classifier.py` and `run_squad.py`) + `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token + types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to + a `sentence B` token (see BERT paper for more details). + `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices + selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max + input sequence length in the current batch. It's the mask that we typically use for attention when + a batch has varying length sentences. + `start_positions`: position of the first token for the labeled span: torch.LongTensor of shape [batch_size]. + Positions are clamped to the length of the sequence and position outside of the sequence are not taken + into account for computing the loss. + `end_positions`: position of the last token for the labeled span: torch.LongTensor of shape [batch_size]. + Positions are clamped to the length of the sequence and position outside of the sequence are not taken + into account for computing the loss. + + Outputs: + if `start_positions` and `end_positions` are not `None`: + Outputs the total_loss which is the sum of the CrossEntropy loss for the start and end token positions. + if `start_positions` or `end_positions` is `None`: + Outputs a tuple of start_logits, end_logits which are the logits respectively for the start and end + position tokens of shape [batch_size, sequence_length]. + + Example usage: + ```python + # Already been converted into WordPiece token ids + input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) + input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) + token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]]) + + config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768, + num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + + model = BertForQuestionAnswering(config) + start_logits, end_logits = model(input_ids, token_type_ids, input_mask) + ``` + """ + def __init__(self, config): + super(BertForQuestionAnswering, self).__init__(config) + self.bert = BertModel(config) + # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version + # self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.qa_outputs = nn.Linear(config.hidden_size, 2) + self.apply(self.init_bert_weights) + + def forward(self, + input_ids, + token_type_ids=None, + attention_mask=None, + start_positions=None, + end_positions=None, + checkpoint_activations=False): + sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1) + end_logits = end_logits.squeeze(-1) + + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions.clamp_(0, ignored_index) + end_positions.clamp_(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + return total_loss + else: + return start_logits, end_logits diff --git a/tests/unit/test_cuda_backward.py b/tests/unit/test_cuda_backward.py new file mode 100755 index 000000000000..b37eb5745817 --- /dev/null +++ b/tests/unit/test_cuda_backward.py @@ -0,0 +1,322 @@ +import argparse +import numpy as np +import torch +import torch.nn.functional as F +import pytest +import json +import random +import time +import copy +from torch import nn +from modelingpreln import BertEncoder as BertEncoderPreln +from modeling import BertEncoder as BertEncoderPostln +from modeling import BertConfig, BertLayerNorm +from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig + +import sys + + +def check_equal(first, second, atol=1e-2, verbose=False): + diction_x = {} + diction_y = {} + + for i, (x, y) in enumerate(zip(first, second)): + print(x[1], y[1]) + + for i, (x, y) in enumerate(zip(first, second)): + k = 0 + while (diction_x.get((k, x[1])) is not None): + k = k + 1 + diction_x[k, x[1]] = x[0] + k = 0 + while (diction_y.get((k, y[1])) is not None): + k = k + 1 + diction_y[k, y[1]] = y[0] + if verbose: + print() + for i, (x, y) in enumerate(zip(diction_x, diction_y)): + print(x, y) + + for i, (x, y) in enumerate(zip(diction_x, diction_y)): + if (x[0] == 1): continue + print("checking ", x[1], ":") + y = diction_y[x[0], x[1]] + x = diction_x[x[0], x[1]] + x = x.cpu().detach().numpy() + y = y.cpu().detach().numpy() + print(x) + print(y) + + avgx = np.sum(abs(x), dtype=float) + countx = x.shape[0] + for i in range(len(x.shape) - 1): + countx *= x.shape[i + 1] + avgx = np.sum(avgx) + tollerance = 1 + if avgx != float('inf') and avgx != -float('inf'): + avgx = avgx / countx + tollerance = avgx * atol + print("tollerance is ", tollerance) + if verbose: + print("x = {}".format(x.flatten())) + print("y = {}".format(y.flatten())) + print('-' * 80) + np.testing.assert_allclose(x, y, err_msg="Index: {}".format(i), atol=tollerance) + + +def zero_grad(variables): + for variable in variables: + variable.grad.zero_() + + +device = torch.device("cuda") +kwargs_fp32 = {'dtype': torch.float, 'device': device, 'requires_grad': True} +kwargs_fp16 = {'dtype': torch.half, 'device': device, 'requires_grad': True} + + +class DSEncoder(nn.Module): + def __init__(self, config, weights, biases): + super(DSEncoder, self).__init__() + self.FinalLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.layer = nn.ModuleList([ + copy.deepcopy(DeepSpeedTransformerLayer(i, + config, + weights, + biases)) + for i in range(config.num_hidden_layers) + ]) + self.grads = [] + self.pre_or_post = config.pre_layer_norm + + def forward(self, + hidden_states, + attention_mask, + output_all_encoded_layers=True, + checkpoint_activations=False): + all_encoder_layers = [] + + def custom(start, end): + def custom_forward(*inputs): + layers = self.layer[start:end] + x_ = inputs[0] + for layer in layers: + x_ = layer(x_, inputs[1]) + return x_ + + return custom_forward + + if checkpoint_activations: + l = 0 + num_layers = len(self.layer) + chunk_length = math.ceil(math.sqrt(num_layers)) + while l < num_layers: + hidden_states = checkpoint.checkpoint(custom(l, + l + chunk_length), + hidden_states, + attention_mask * 1) + l += chunk_length + # decoder layers + else: + for i, layer_module in enumerate(self.layer): + hidden_states = layer_module(hidden_states, attention_mask, self.grads) + hidden_states.register_hook( + lambda x, + self=self: self.grads.append([x, + "hidden_state"])) + + if output_all_encoded_layers: + all_encoder_layers.append(hidden_states) + + if not output_all_encoded_layers or checkpoint_activations: + if (self.pre_or_post): + hidden_states = self.FinalLayerNorm(hidden_states) + all_encoder_layers.append(hidden_states) + return all_encoder_layers + + def get_grads(self): + return self.grads + + +def create_models(ds_config): + bert_config = BertConfig(vocab_size_or_config_json_file=119547, + hidden_size=ds_config.hidden_size, + num_hidden_layers=ds_config.num_hidden_layers, + num_attention_heads=ds_config.heads, + intermediate_size=4 * ds_config.hidden_size, + hidden_act="gelu", + hidden_dropout_prob=ds_config.hidden_dropout_ratio, + attention_probs_dropout_prob=ds_config.attn_dropout_ratio, + max_position_embeddings=ds_config.max_seq_length, + type_vocab_size=2, + initializer_range=ds_config.initializer_range) + + weights = [] + biases = [] + + for i in range(4): + weights.append( + nn.Parameter(torch.Tensor(ds_config.hidden_size, + ds_config.hidden_size))) + weights[i].data.normal_(mean=0.0, std=ds_config.initializer_range) + + weights.append(nn.Parameter(torch.Tensor(ds_config.hidden_size))) + weights[4].data.fill_(1.0) + weights.append( + nn.Parameter(torch.Tensor(4 * ds_config.hidden_size, + ds_config.hidden_size))) + weights[5].data.normal_(mean=0.0, std=ds_config.initializer_range) + weights.append( + nn.Parameter(torch.Tensor(ds_config.hidden_size, + 4 * ds_config.hidden_size))) + weights[6].data.normal_(mean=0.0, std=ds_config.initializer_range) + weights.append(nn.Parameter(torch.Tensor(ds_config.hidden_size))) + weights[7].data.fill_(1.0) + + biases.append(nn.Parameter(torch.Tensor(ds_config.hidden_size))) + biases[0].data.zero_() + for i in range(4): + biases.append(nn.Parameter(torch.Tensor(ds_config.hidden_size))) + biases[i + 1].data.zero_() + biases.append(nn.Parameter(torch.Tensor(4 * ds_config.hidden_size))) + biases[5].data.zero_() + biases.append(nn.Parameter(torch.Tensor(ds_config.hidden_size))) + biases[6].data.zero_() + biases.append(nn.Parameter(torch.Tensor(ds_config.hidden_size))) + biases[7].data.zero_() + + if (ds_config.pre_layer_norm): + bert_encoder = BertEncoderPreln(bert_config, weights, biases) + else: + bert_encoder = BertEncoderPostln(bert_config, weights, biases) + ds_encoder = DSEncoder(ds_config, weights, biases) + + if ds_config.fp16: + bert_encoder.half() + ds_encoder.half() + + bert_encoder.cuda() + ds_encoder.cuda() + + return bert_encoder, ds_encoder + + +def set_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + +def run_backward(ds_config, atol=1e-2, verbose=False): + set_seed(123) + bert_encoder, ds_encoder = create_models(ds_config) + + # prepare test data + kwargs = kwargs_fp16 if ds_config.fp16 else kwargs_fp32 + hidden_states = torch.randn(ds_config.batch_size, + ds_config.max_seq_length, + ds_config.hidden_size, + **kwargs) + input_mask = torch.randn(ds_config.batch_size, + 1, + 1, + ds_config.max_seq_length, + **kwargs) + Y = torch.randn(ds_config.batch_size, + ds_config.max_seq_length, + ds_config.hidden_size, + **kwargs) + + # run baseline + base_results = bert_encoder(hidden_states, + input_mask, + output_all_encoded_layers=False, + checkpoint_activations=False) + + loss = (Y - base_results[0]).pow(2).sum() + loss.backward() + base_grads = bert_encoder.get_grads() + + # run ds + ds_results = ds_encoder(hidden_states, + input_mask, + output_all_encoded_layers=False, + checkpoint_activations=False) + + loss = (Y - ds_results[0]).pow(2).sum() + loss.backward() + ds_grads = ds_encoder.get_grads() + + # check grads + check_equal(base_grads, ds_grads, atol=atol, verbose=verbose) + + +@pytest.mark.parametrize('batch_size, hidden_size, seq_len, heads, num_layers, is_preln, use_fp16, atol', + [ + (3,1024,128,16,24,True,False, 0.05), + (3,1024,128,16,24,True,True, 0.05), + (3,1024,128,16,24,False,False, 0.1), + (3,1024,128,16,24,False,True, 0.2), + ]) # yapf: disable +def test_backward(batch_size, + hidden_size, + seq_len, + heads, + num_layers, + is_preln, + use_fp16, + atol): + # Only run fp16 test cases on devices with 7+ capability. + major, _ = torch.cuda.get_device_capability() + if major < 7 and (use_fp16 is True or is_preln is False): + return + + ds_config = DeepSpeedTransformerConfig() + ds_config.layer_id = None + ds_config.batch_size = batch_size + ds_config.hidden_size = hidden_size + ds_config.max_seq_length = seq_len + ds_config.heads = heads + ds_config.attn_dropout_ratio = 0.0 + ds_config.hidden_dropout_ratio = 0.0 + ds_config.num_hidden_layers = num_layers + ds_config.pre_layer_norm = is_preln + ds_config.initializer_range = 0.02 + ds_config.fp16 = use_fp16 + + run_backward(ds_config, atol=atol) + +@pytest.mark.parametrize('batch_size, hidden_size, seq_len, heads, num_layers, is_preln, use_fp16, atol', + [ + (3,1024,128,16,24,True,False, 0.07), + (3,1024,128,16,24,True,True, 0.05), + (3,1024,128,16,24,False,False, 0.1), + (3,1024,128,16,24,False,True, 0.2), + ]) # yapf: disable +def test_backward_stochastic(batch_size, + hidden_size, + seq_len, + heads, + num_layers, + is_preln, + use_fp16, + atol): + # Only run fp16 test cases on devices with 7+ capability. + major, _ = torch.cuda.get_device_capability() + if major < 7 and (use_fp16 is True or is_preln is False): + return + + ds_config = DeepSpeedTransformerConfig() + ds_config.layer_id = None + ds_config.batch_size = batch_size + ds_config.hidden_size = hidden_size + ds_config.max_seq_length = seq_len + ds_config.heads = heads + ds_config.attn_dropout_ratio = 0.0 + ds_config.hidden_dropout_ratio = 0.0 + ds_config.num_hidden_layers = num_layers + ds_config.pre_layer_norm = is_preln + ds_config.initializer_range = 0.02 + ds_config.fp16 = use_fp16 + ds_config.stochastic_mode = True + + run_backward(ds_config, atol=atol) diff --git a/tests/unit/test_cuda_forward.py b/tests/unit/test_cuda_forward.py new file mode 100755 index 000000000000..4e995a34448f --- /dev/null +++ b/tests/unit/test_cuda_forward.py @@ -0,0 +1,325 @@ +import argparse +import numpy as np +import torch +import torch.nn.functional as F +import pytest +import json +import random +import time +import copy +from torch import nn +from modelingpreln import BertEncoder as BertEncoderPreln +from modeling import BertEncoder as BertEncoderPostln +from modeling import BertLayerNorm, BertConfig +from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig + +import sys + + +def check_equal(first, second, atol=1e-2, verbose=False): + if verbose: + print() + for i, (x, y) in enumerate(zip(first, second)): + x = x[0].cpu().detach().numpy() + y = y[0].cpu().detach().numpy() + if verbose: + print("x = {}".format(x.flatten())) + print("y = {}".format(y.flatten())) + print('-' * 80) + np.testing.assert_allclose(x, y, err_msg="Index: {}".format(i), atol=atol) + + +def zero_grad(variables): + for variable in variables: + variable.grad.zero_() + + +device = torch.device("cuda") +kwargs_fp32 = {'dtype': torch.float, 'device': device, 'requires_grad': True} +kwargs_fp16 = {'dtype': torch.half, 'device': device, 'requires_grad': True} + + +class DSEncoder(nn.Module): + def __init__(self, config, weights, biases): + super(DSEncoder, self).__init__() + self.FinalLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) + self.layer = nn.ModuleList([ + copy.deepcopy(DeepSpeedTransformerLayer(i, + config, + weights, + biases)) + for i in range(config.num_hidden_layers) + ]) + self.grads = [] + self.pre_or_post = config.pre_layer_norm + + def forward(self, + hidden_states, + attention_mask, + output_all_encoded_layers=True, + checkpoint_activations=False): + all_encoder_layers = [] + + def custom(start, end): + def custom_forward(*inputs): + layers = self.layer[start:end] + x_ = inputs[0] + for layer in layers: + x_ = layer(x_, inputs[1]) + return x_ + + return custom_forward + + if checkpoint_activations: + l = 0 + num_layers = len(self.layer) + chunk_length = math.ceil(math.sqrt(num_layers)) + while l < num_layers: + hidden_states = checkpoint.checkpoint(custom(l, + l + chunk_length), + hidden_states, + attention_mask * 1) + l += chunk_length + # decoder layers + else: + for i, layer_module in enumerate(self.layer): + hidden_states = layer_module(hidden_states, attention_mask) + hidden_states.register_hook( + lambda x, + i=i, + self=self: self.grads.append([x, + "hidden_state"])) + + if output_all_encoded_layers: + all_encoder_layers.append(hidden_states) + + if not output_all_encoded_layers or checkpoint_activations: + if (self.pre_or_post): + hidden_states = self.FinalLayerNorm(hidden_states) + all_encoder_layers.append(hidden_states) + return all_encoder_layers + + def get_grads(self): + return self.grads + + +def create_models(ds_config): + bert_config = BertConfig(vocab_size_or_config_json_file=119547, + hidden_size=ds_config.hidden_size, + num_hidden_layers=ds_config.num_hidden_layers, + num_attention_heads=ds_config.heads, + batch_size=ds_config.batch_size, + intermediate_size=4 * ds_config.hidden_size, + hidden_act="gelu", + hidden_dropout_prob=ds_config.hidden_dropout_ratio, + attention_probs_dropout_prob=ds_config.attn_dropout_ratio, + max_position_embeddings=ds_config.max_seq_length, + type_vocab_size=2, + initializer_range=ds_config.initializer_range, + fp16=ds_config.fp16) + + weights = [] + biases = [] + + for i in range(4): + weights.append( + nn.Parameter(torch.Tensor(ds_config.hidden_size, + ds_config.hidden_size))) + weights[i].data.normal_(mean=0.0, std=ds_config.initializer_range) + + weights.append(nn.Parameter(torch.Tensor(ds_config.hidden_size))) + weights[4].data.fill_(1.0) + weights.append( + nn.Parameter(torch.Tensor(4 * ds_config.hidden_size, + ds_config.hidden_size))) + weights[5].data.normal_(mean=0.0, std=ds_config.initializer_range) + weights.append( + nn.Parameter(torch.Tensor(ds_config.hidden_size, + 4 * ds_config.hidden_size))) + weights[6].data.normal_(mean=0.0, std=ds_config.initializer_range) + weights.append(nn.Parameter(torch.Tensor(ds_config.hidden_size))) + weights[7].data.fill_(1.0) + + biases.append(nn.Parameter(torch.Tensor(ds_config.hidden_size))) + biases[0].data.zero_() + for i in range(4): + biases.append(nn.Parameter(torch.Tensor(ds_config.hidden_size))) + biases[i + 1].data.zero_() + biases.append(nn.Parameter(torch.Tensor(4 * ds_config.hidden_size))) + biases[5].data.zero_() + biases.append(nn.Parameter(torch.Tensor(ds_config.hidden_size))) + biases[6].data.zero_() + biases.append(nn.Parameter(torch.Tensor(ds_config.hidden_size))) + biases[7].data.zero_() + + if (ds_config.pre_layer_norm): + bert_encoder = BertEncoderPreln(bert_config, weights, biases) + else: + bert_encoder = BertEncoderPostln(bert_config, weights, biases) + ds_encoder = DSEncoder(ds_config, weights, biases) + + if ds_config.fp16: + bert_encoder.half() + ds_encoder.half() + + bert_encoder.cuda() + ds_encoder.cuda() + + return bert_encoder, ds_encoder + + +def set_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + +def run_forward(ds_config, atol=1e-2, verbose=False, test_bsz=None): + set_seed(123) + bert_encoder, ds_encoder = create_models(ds_config) + + bsz = ds_config.batch_size if test_bsz is None else test_bsz + + # prepare test data + kwargs = kwargs_fp16 if ds_config.fp16 else kwargs_fp32 + hidden_states = torch.randn(bsz, + ds_config.max_seq_length, + ds_config.hidden_size, + **kwargs) + input_mask = torch.randn(bsz, 1, 1, ds_config.max_seq_length, **kwargs) + + # run baseline + base_results = bert_encoder(hidden_states, + input_mask, + output_all_encoded_layers=False, + checkpoint_activations=False) + + # run ds + ds_results = ds_encoder(hidden_states, + input_mask, + output_all_encoded_layers=False, + checkpoint_activations=False) + + # check grads + check_equal(base_results, ds_results, atol=atol, verbose=verbose) + + +# FP16 test cases can only run on the devices support FP16. +@pytest.mark.parametrize('batch_size, hidden_size, seq_len, heads, num_layers, is_preln, use_fp16', + [ + (64,1024,128,16,3,True,False), + (64,1024,128,16,3,True,True), + (8,1024,384,16,3,True,False), + (8,1024,384,16,3,True,True), + (8,1024,512,16,3,True,False), + (8,1024,512,16,3,True,True), + (64,1024,128,16,3,False,False), + (64,1024,128,16,3,False,True), + (8,1024,384,16,3,False,False), + (8,1024,384,16,3,False,True), + (8,1024,512,16,3,False,False), + (8,1024,512,16,3,False,True), + (8,1536,128,24,3,False,False), + (8,1536,128,24,3,False,True), + (8,2048,128,32,3,False,False), + (8,2048,128,32,3,False,True), + (8,2560,128,40,3,False,False), + (8,2560,128,40,3,False,True), + ]) # yapf: disable +def test_forward(batch_size, + hidden_size, + seq_len, + heads, + num_layers, + is_preln, + use_fp16): + # Only run fp16 test cases on devices with 7+ capability. + major, _ = torch.cuda.get_device_capability() + if major < 7 and use_fp16 is True: + return + + ds_config = DeepSpeedTransformerConfig() + ds_config.layer_id = None + ds_config.batch_size = batch_size + ds_config.hidden_size = hidden_size + ds_config.max_seq_length = seq_len + ds_config.heads = heads + ds_config.attn_dropout_ratio = 0.0 + ds_config.hidden_dropout_ratio = 0.0 + ds_config.num_hidden_layers = num_layers + ds_config.pre_layer_norm = is_preln + ds_config.initializer_range = 0.02 + ds_config.fp16 = use_fp16 + + run_forward(ds_config, atol=2e-2) + + +@pytest.mark.parametrize('batch_size, small_bsz, hidden_size, seq_len, heads, num_layers, is_preln, use_fp16', + [ + (8,3,1024,512,16,3,True,False), + (8,7,1024,512,16,3,True,True), + (8,3,1024,512,16,3,False,False), + (8,7,1024,512,16,3,False,True), + ]) # yapf: disable +def test_forward_with_small_bsz(batch_size, + small_bsz, + hidden_size, + seq_len, + heads, + num_layers, + is_preln, + use_fp16): + # Only run fp16 test cases on devices with 7+ capability. + major, _ = torch.cuda.get_device_capability() + if major < 7 and use_fp16 is True: + return + + ds_config = DeepSpeedTransformerConfig() + ds_config.layer_id = None + ds_config.batch_size = batch_size + ds_config.hidden_size = hidden_size + ds_config.max_seq_length = seq_len + ds_config.heads = heads + ds_config.attn_dropout_ratio = 0.0 + ds_config.hidden_dropout_ratio = 0.0 + ds_config.num_hidden_layers = num_layers + ds_config.pre_layer_norm = is_preln + ds_config.initializer_range = 0.02 + ds_config.fp16 = use_fp16 + + run_forward(ds_config, atol=2e-2, test_bsz=small_bsz) + +@pytest.mark.parametrize('batch_size, hidden_size, seq_len, heads, num_layers, is_preln, use_fp16', + [ + (64,1024,128,16,3,True,False), + (64,1024,128,16,3,True,True), + (64,1024,128,16,3,False,False), + (64,1024,128,16,3,False,True), + ]) # yapf: disable +def test_forward_stochastic(batch_size, + hidden_size, + seq_len, + heads, + num_layers, + is_preln, + use_fp16): + # Only run fp16 test cases on devices with 7+ capability. + major, _ = torch.cuda.get_device_capability() + if major < 7 and use_fp16 is True: + return + + ds_config = DeepSpeedTransformerConfig() + ds_config.layer_id = None + ds_config.batch_size = batch_size + ds_config.hidden_size = hidden_size + ds_config.max_seq_length = seq_len + ds_config.heads = heads + ds_config.attn_dropout_ratio = 0.0 + ds_config.hidden_dropout_ratio = 0.0 + ds_config.num_hidden_layers = num_layers + ds_config.pre_layer_norm = is_preln + ds_config.initializer_range = 0.02 + ds_config.fp16 = use_fp16 + ds_config.stochastic_mode = True + + run_forward(ds_config, atol=7e-2)

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