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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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54
colossalai/kernel/triton/qkv_matmul_kernel.py
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@@ -1,7 +1,7 @@
import torch
try:
import triton
import triton.language as tl
HAS_TRITON = True
except ImportError:
HAS_TRITON = False
@@ -9,9 +9,10 @@ except ImportError:
if HAS_TRITON:
'''
"""
this kernel function is modified from https://triton-lang.org/main/getting-started/tutorials/03-matrix-multiplication.html
'''
"""
@triton.jit
def qkv_gemm_4d_kernel(
a_ptr,
@@ -34,12 +35,12 @@ if HAS_TRITON:
stride_cn,
scale,
# Meta-parameters
BLOCK_SIZE_M : tl.constexpr = 64,
BLOCK_SIZE_N : tl.constexpr = 32,
BLOCK_SIZE_K : tl.constexpr = 32,
GROUP_SIZE_M : tl.constexpr = 8,
BLOCK_SIZE_M: tl.constexpr = 64,
BLOCK_SIZE_N: tl.constexpr = 32,
BLOCK_SIZE_K: tl.constexpr = 32,
GROUP_SIZE_M: tl.constexpr = 8,
):
r""" A kernel function which is used to do batch-matmul for Q*K^T or score_matrix * V for attention layer,
r"""A kernel function which is used to do batch-matmul for Q*K^T or score_matrix * V for attention layer,
where score_matrix is softmax(Q*V^T/sqrt(hidden_size))
Args:
a_ptr(torch.Tensor): pointer to input tensor array (bs, M, h, K) or (bs, h, M, K)
@@ -53,21 +54,21 @@ if HAS_TRITON:
stride_bh(tl.constexpr): stride for h-dimention for tensor array B
stride_bk(tl.constexpr): stride for k-dimention for tensor array B
stride_bn(tl.constexpr): stride for n-dimention for tensor array B
stride_cb(tl.constexpr): stride for bs-dimention for tensor array output
stride_cb(tl.constexpr): stride for bs-dimention for tensor array output
stride_ch(tl.constexpr): stride for h-dimention for tensor array output
stride_cm(tl.constexpr): stride for m-dimention for tensor array output
stride_cn(tl.constexpr): stride for n-dimention for tensor array output
BLOCK_SIZE_M : tiling size for M-dimension of tensor Array a
BLOCK_SIZE_N : tiling size for N-dimension of tensor Array b
BLOCK_SIZE_K : tiling size for K-dimension of a and b
GROUP_SIZE_M : group size for reducing cache miss, more details:
GROUP_SIZE_M : group size for reducing cache miss, more details:
"""
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
batch = tl.program_id(axis = 0)
head = tl.program_id(axis = 1)
pid = tl.program_id(axis = 2)
batch = tl.program_id(axis=0)
head = tl.program_id(axis=1)
pid = tl.program_id(axis=2)
# the following is from tutorial: https://triton-lang.org/main/getting-started/tutorials/03-matrix-multiplication.html
num_pid_in_group = GROUP_SIZE_M * num_pid_n
@@ -77,33 +78,38 @@ if HAS_TRITON:
pid_m = first_pid_m + (pid % group_size_m)
pid_n = (pid % num_pid_in_group) // group_size_m
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = (a_ptr + batch * stride_ab + head * stride_ah +
(offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak))
b_ptrs = (b_ptr + batch * stride_bb + head * stride_bh +
(offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn))
a_ptrs = (
a_ptr + batch * stride_ab + head * stride_ah + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
)
b_ptrs = (
b_ptr + batch * stride_bb + head * stride_bh + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
)
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, K, BLOCK_SIZE_K):
a_mask = (offs_am[:, None] < M) & (offs_k[None, :] + k < K)
b_mask = (offs_k[:, None] + k < K) & (offs_bn[None, :] < N)
a = tl.load(a_ptrs, mask=a_mask, other=0.)
b = tl.load(b_ptrs, mask=b_mask, other=0.)
a = tl.load(a_ptrs, mask=a_mask, other=0.0)
b = tl.load(b_ptrs, mask=b_mask, other=0.0)
accumulator += tl.dot(a, b)
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
accumulator = accumulator.to(c_ptr.dtype.element_ty)
if scale > 0:
accumulator = accumulator * scale.to(c_ptr.dtype.element_ty)
offs_accumu_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_accumu_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = (c_ptr + batch * stride_cb + head * stride_ch + stride_cm * offs_accumu_m[:, None] +
stride_cn * offs_accumu_n[None, :])
c_ptrs = (
c_ptr
+ batch * stride_cb
+ head * stride_ch
+ stride_cm * offs_accumu_m[:, None]
+ stride_cn * offs_accumu_n[None, :]
)
accumulator_mask = (offs_accumu_m[:, None] < M) & (offs_accumu_n[None, :] < N)
tl.store(c_ptrs, accumulator, mask=accumulator_mask)