ColossalAI/colossalai/kernel/triton/fused_layernorm.py

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[Feature] The first PR to Add TP inference engine, kv-cache manager and related kernels for our inference system (#4577) * [infer] Infer/llama demo (#4503) * add * add infer example * finish * finish * stash * fix * [Kernels] add inference token attention kernel (#4505) * add token forward * fix tests * fix comments * add try import triton * add adapted license * add tests check * [Kernels] add necessary kernels (llama & bloom) for attention forward and kv-cache manager (#4485) * added _vllm_rms_norm * change place * added tests * added tests * modify * adding kernels * added tests: * adding kernels * modify * added * updating kernels * adding tests * added tests * kernel change * submit * modify * added * edit comments * change name * change commnets and fix import * add * added * combine codes (#4509) * [feature] add KV cache manager for llama & bloom inference (#4495) * add kv cache memory manager * add stateinfo during inference * format * format * rename file * add kv cache test * revise on BatchInferState * file dir change * [Bug FIx] import llama context ops fix (#4524) * added _vllm_rms_norm * change place * added tests * added tests * modify * adding kernels * added tests: * adding kernels * modify * added * updating kernels * adding tests * added tests * kernel change * submit * modify * added * edit comments * change name * change commnets and fix import * add * added * fix * add ops into init.py * add * [Infer] Add TPInferEngine and fix file path (#4532) * add engine for TP inference * move file path * update path * fix TPInferEngine * remove unused file * add engine test demo * revise TPInferEngine * fix TPInferEngine, add test * fix * Add Inference test for llama (#4508) * add kv cache memory manager * add stateinfo during inference * add * add infer example * finish * finish * format * format * rename file * add kv cache test * revise on BatchInferState * add inference test for llama * fix conflict * feature: add some new features for llama engine * adapt colossalai triton interface * Change the parent class of llama policy * add nvtx * move llama inference code to tensor_parallel * fix __init__.py * rm tensor_parallel * fix: fix bugs in auto_policy.py * fix:rm some unused codes * mv colossalai/tpinference to colossalai/inference/tensor_parallel * change __init__.py * save change * fix engine * Bug fix: Fix hang * remove llama_infer_engine.py --------- Co-authored-by: yuanheng-zhao <jonathan.zhaoyh@gmail.com> Co-authored-by: CjhHa1 <cjh18671720497@outlook.com> * [infer] Add Bloom inference policy and replaced methods (#4512) * add bloom inference methods and policy * enable pass BatchInferState from model forward * revise bloom infer layers/policies * add engine for inference (draft) * add test for bloom infer * fix bloom infer policy and flow * revise bloom test * fix bloom file path * remove unused codes * fix bloom modeling * fix dir typo * fix trivial * fix policy * clean pr * trivial fix * Revert "[infer] Add Bloom inference policy and replaced methods (#4512)" (#4552) This reverts commit 17cfa5714083a81a505c097f1c411cd28162d922. * [Doc] Add colossal inference doc (#4549) * create readme * add readme.md * fix typos * [infer] Add Bloom inference policy and replaced methods (#4553) * add bloom inference methods and policy * enable pass BatchInferState from model forward * revise bloom infer layers/policies * add engine for inference (draft) * add test for bloom infer * fix bloom infer policy and flow * revise bloom test * fix bloom file path * remove unused codes * fix bloom modeling * fix dir typo * fix trivial * fix policy * clean pr * trivial fix * trivial * Fix Bugs In Llama Model Forward (#4550) * add kv cache memory manager * add stateinfo during inference * add * add infer example * finish * finish * format * format * rename file * add kv cache test * revise on BatchInferState * add inference test for llama * fix conflict * feature: add some new features for llama engine * adapt colossalai triton interface * Change the parent class of llama policy * add nvtx * move llama inference code to tensor_parallel * fix __init__.py * rm tensor_parallel * fix: fix bugs in auto_policy.py * fix:rm some unused codes * mv colossalai/tpinference to colossalai/inference/tensor_parallel * change __init__.py * save change * fix engine * Bug fix: Fix hang * remove llama_infer_engine.py * bug fix: fix bugs about infer_state.is_context_stage * remove pollcies * fix: delete unused code * fix: delete unused code * remove unused coda * fix conflict --------- Co-authored-by: yuanheng-zhao <jonathan.zhaoyh@gmail.com> Co-authored-by: CjhHa1 <cjh18671720497@outlook.com> * [doc] add colossal inference fig (#4554) * create readme * add readme.md * fix typos * upload fig * [NFC] fix docstring for colossal inference (#4555) Fix docstring and comments in kv cache manager and bloom modeling * fix docstring in llama modeling (#4557) * [Infer] check import vllm (#4559) * change import vllm * import apply_rotary_pos_emb * change import location * [DOC] add installation req (#4561) * add installation req * fix * slight change * remove empty * [Feature] rms-norm transfer into inference llama.py (#4563) * add installation req * fix * slight change * remove empty * add rmsnorm polciy * add * clean codes * [infer] Fix tp inference engine (#4564) * fix engine prepare data * add engine test * use bloom for testing * revise on test * revise on test * reset shardformer llama (#4569) * [infer] Fix engine - tensors on different devices (#4570) * fix diff device in engine * [codefactor] Feature/colossal inference (#4579) * code factors * remove * change coding (#4581) * [doc] complete README of colossal inference (#4585) * complete fig * Update README.md * [doc]update readme (#4586) * update readme * Update README.md * bug fix: fix bus in llama and bloom (#4588) * [BUG FIX]Fix test engine in CI and non-vllm kernels llama forward (#4592) * fix tests * clean * clean * fix bugs * add * fix llama non-vllm kernels bug * modify * clean codes * [Kernel]Rmsnorm fix (#4598) * fix tests * clean * clean * fix bugs * add * fix llama non-vllm kernels bug * modify * clean codes * add triton rmsnorm * delete vllm kernel flag * [Bug Fix]Fix bugs in llama (#4601) * fix tests * clean * clean * fix bugs * add * fix llama non-vllm kernels bug * modify * clean codes * bug fix: remove rotary_positions_ids --------- Co-authored-by: cuiqing.li <lixx3527@gmail.com> * [kernel] Add triton layer norm & replace norm for bloom (#4609) * add layernorm for inference * add test for layernorm kernel * add bloom layernorm replacement policy * trivial: path * [Infer] Bug fix rotary embedding in llama (#4608) * fix rotary embedding * delete print * fix init seq len bug * rename pytest * add benchmark for llama * refactor codes * delete useless code * [bench] Add bloom inference benchmark (#4621) * add bloom benchmark * readme - update benchmark res * trivial - uncomment for testing (#4622) * [Infer] add check triton and cuda version for tests (#4627) * fix rotary embedding * delete print * fix init seq len bug * rename pytest * add benchmark for llama * refactor codes * delete useless code * add check triton and cuda * Update sharder.py (#4629) * [Inference] Hot fix some bugs and typos (#4632) * fix * fix test * fix conflicts * [typo]Comments fix (#4633) * fallback * fix commnets * bug fix: fix some bugs in test_llama and test_bloom (#4635) * [Infer] delete benchmark in tests and fix bug for llama and bloom (#4636) * fix rotary embedding * delete print * fix init seq len bug * rename pytest * add benchmark for llama * refactor codes * delete useless code * add check triton and cuda * delete benchmark and fix infer bugs * delete benchmark for tests * delete useless code * delete bechmark function in utils * [Fix] Revise TPInferEngine, inference tests and benchmarks (#4642) * [Fix] revise TPInferEngine methods and inference tests * fix llama/bloom infer benchmarks * fix infer tests * trivial fix: benchmakrs * trivial * trivial: rm print * modify utils filename for infer ops test (#4657) * [Infer] Fix TPInferEngine init & inference tests, benchmarks (#4670) * fix engine funcs * TPInferEngine: receive shard config in init * benchmarks: revise TPInferEngine init * benchmarks: remove pytest decorator * trivial fix * use small model for tests * [NFC] use args for infer benchmarks (#4674) * revise infer default (#4683) * [Fix] optimize/shard model in TPInferEngine init (#4684) * remove using orig model in engine * revise inference tests * trivial: rename --------- Co-authored-by: Jianghai <72591262+CjhHa1@users.noreply.github.com> Co-authored-by: Xu Kai <xukai16@foxmail.com> Co-authored-by: Yuanheng Zhao <54058983+yuanheng-zhao@users.noreply.github.com> Co-authored-by: yuehuayingxueluo <867460659@qq.com> Co-authored-by: yuanheng-zhao <jonathan.zhaoyh@gmail.com> Co-authored-by: CjhHa1 <cjh18671720497@outlook.com>
2023-09-11 17:22:56 +00:00
import torch
try:
import triton
import triton.language as tl
HAS_TRITON = True
except ImportError:
HAS_TRITON = False
print("please install triton from https://github.com/openai/triton")
if HAS_TRITON:
# CREDITS: These functions are adapted from the Triton tutorial
# https://triton-lang.org/main/getting-started/tutorials/05-layer-norm.html
@triton.jit
def _layer_norm_fwd_fused(
X, # pointer to the input
Y, # pointer to the output
W, # pointer to the weights
B, # pointer to the biases
stride, # how much to increase the pointer when moving by 1 row
N, # number of columns in X
eps, # epsilon to avoid division by zero
BLOCK_SIZE: tl.constexpr,
):
# Map the program id to the row of X and Y it should compute.
row = tl.program_id(0)
Y += row * stride
X += row * stride
# Compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# Compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
x = tl.where(cols < N, x - mean, 0.)
_var += x * x
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# Normalize and apply linear transformation
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
w = tl.load(W + cols, mask=mask)
b = tl.load(B + cols, mask=mask)
x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
x_hat = (x - mean) * rstd
y = x_hat * w + b
# Write output
tl.store(Y + cols, y.to(tl.float16), mask=mask)
@torch.no_grad()
def layer_norm(x, weight, bias, eps):
# allocate output
y = torch.empty_like(x)
# reshape input data into 2D tensor
x_arg = x.reshape(-1, x.shape[-1])
M, N = x_arg.shape
# Less than 64KB per feature: enqueue fused kernel
MAX_FUSED_SIZE = 65536 // x.element_size()
BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
if N > BLOCK_SIZE:
raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
# heuristics for number of warps
num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
# enqueue kernel
_layer_norm_fwd_fused[(M,)](x_arg,
y,
weight,
bias,
x_arg.stride(0),
N,
eps,
BLOCK_SIZE=BLOCK_SIZE,
num_warps=num_warps)
return y