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[CI/Build] Enforce style for C++ and CUDA code with clang-format (#4722)

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This commit is contained in:
Michael Goin
2024-05-22 03:18:41 -04:00
committed by GitHub
parent 9b9a10d6cb
commit 5f6d10c14c
64 changed files with 6398 additions and 6790 deletions
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75
csrc/cpu/pybind.cpp
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@@ -8,66 +8,37 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
pybind11::module ops = m.def_submodule("ops", "vLLM custom operators");
// Attention ops
ops.def(
"paged_attention_v1",
&paged_attention_v1,
"Compute the attention between an input query and the cached keys/values using PagedAttention.");
ops.def(
"paged_attention_v2",
&paged_attention_v2,
"PagedAttention V2.");
ops.def("paged_attention_v1", &paged_attention_v1,
"Compute the attention between an input query and the cached "
"keys/values using PagedAttention.");
ops.def("paged_attention_v2", &paged_attention_v2, "PagedAttention V2.");
// Activation ops
ops.def(
"silu_and_mul",
&silu_and_mul,
"Activation function used in SwiGLU.");
ops.def(
"gelu_and_mul",
&gelu_and_mul,
"Activation function used in GeGLU with `none` approximation.");
ops.def(
"gelu_tanh_and_mul",
&gelu_tanh_and_mul,
"Activation function used in GeGLU with `tanh` approximation.");
ops.def(
"gelu_new",
&gelu_new,
"GELU implementation used in GPT-2.");
ops.def(
"gelu_fast",
&gelu_fast,
"Approximate GELU implementation.");
ops.def("silu_and_mul", &silu_and_mul, "Activation function used in SwiGLU.");
ops.def("gelu_and_mul", &gelu_and_mul,
"Activation function used in GeGLU with `none` approximation.");
ops.def("gelu_tanh_and_mul", &gelu_tanh_and_mul,
"Activation function used in GeGLU with `tanh` approximation.");
ops.def("gelu_new", &gelu_new, "GELU implementation used in GPT-2.");
ops.def("gelu_fast", &gelu_fast, "Approximate GELU implementation.");
// Layernorm
ops.def(
"rms_norm",
&rms_norm,
"Apply Root Mean Square (RMS) Normalization to the input tensor.");
ops.def("rms_norm", &rms_norm,
"Apply Root Mean Square (RMS) Normalization to the input tensor.");
ops.def(
"fused_add_rms_norm",
&fused_add_rms_norm,
"In-place fused Add and RMS Normalization");
ops.def("fused_add_rms_norm", &fused_add_rms_norm,
"In-place fused Add and RMS Normalization");
// Rotary embedding
ops.def(
"rotary_embedding",
&rotary_embedding,
"Apply GPT-NeoX or GPT-J style rotary embedding to query and key");
ops.def("rotary_embedding", &rotary_embedding,
"Apply GPT-NeoX or GPT-J style rotary embedding to query and key");
// Cache ops
pybind11::module cache_ops = m.def_submodule("cache_ops", "vLLM cache ops");
cache_ops.def(
"swap_blocks",
&swap_blocks,
"Swap in (out) the cache blocks from src to dst");
cache_ops.def(
"copy_blocks",
&copy_blocks,
"Copy the cache blocks from src to dst");
cache_ops.def(
"reshape_and_cache",
&reshape_and_cache,
"Reshape the key and value tensors and cache them");
cache_ops.def("swap_blocks", &swap_blocks,
"Swap in (out) the cache blocks from src to dst");
cache_ops.def("copy_blocks", &copy_blocks,
"Copy the cache blocks from src to dst");
cache_ops.def("reshape_and_cache", &reshape_and_cache,
"Reshape the key and value tensors and cache them");
}