if N <= 1024: GROUP_SIZE_M = 256 # allocate output # 分配输出 locks = torch.zeros(2 * GROUP_SIZE_M, dtype=torch.int32, device=w.device) _dw = torch.zeros((GROUP_SIZE_M, N), dtype=x.dtype, device=w.device) _db = torch.zeros((GROUP_SIZE_M, N), dtype=x.dtype, device=w...
group_id = pid // num_pid_in_group # Row-id of the first program in the group # 组内第一个程序的行 id first_pid_m = group_id * GROUP_SIZE_M # If `num_pid_m` isn't divisible by `GROUP_SIZE_M`, the last group is smaller # 如果 `num_pid_m` 不能被 `GROUP_SIZE_M` ...
num_pid_n=tl.cdiv(N,BLOCK_SIZE_N) num_pid_in_group=GROUP_SIZE_M*num_pid_n group_id=pid//num_pid_in_group first_pid_m=group_id*GROUP_SIZE_M group_size_m=min(num_pid_m-first_pid_m,GROUP_SIZE_M) pid_m=first_pid_m+(pid%group_size_m) pid_n=(pid%num_pid_in_group)//gr...
triton.Config({'BLOCK_SIZE_M':128,'BLOCK_SIZE_N':256,'BLOCK_SIZE_K':64,'GROUP_SIZE_M':8},num_stages=3,num_warps=8), triton.Config({'BLOCK_SIZE_M':64,'BLOCK_SIZE_N':256,'BLOCK_SIZE_K':32,'GROUP_SIZE_M':8},num_stages=4,num_warps=4), triton.Config({'BLOCK_SIZE_M':1...
# 并行进行, 最外层循环是以对矩阵A以行来分块的尺寸BLOCK_SIZE_M为单位的 for m in range(0, M, BLOCK_SIZE_M): # 并行进行, 第二层和第一层循环共同决定计算结果在结果矩阵C中的位置 for n in range(0, N, BLOCK_SIZE_N): acc = zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=float32) fo...
BLOCK_SIZE_K:tl.constexpr,# GROUP_SIZE_M:tl.constexpr,#):pid=tl.program_id(axis=0)num_pid_m=tl.cdiv(M,BLOCK_SIZE_M)num_pid_n=tl.cdiv(N,BLOCK_SIZE_N)num_pid_in_group=GROUP_SIZE_M*num_pid_n group_id=pid// num_pid_in_groupfirst_pid_m=group_id*GROUP_SIZE_M ...
GROUP_SIZE_M:tl.constexpr,BLOCK_SIZE_N:tl.constexpr):#Map the program id to the elements of X,DX,and DY it should compute.# 映射程序 id 到对应计算的 X,DX,DY row=tl.program_id(0)cols=tl.arange(0,BLOCK_SIZE_N)mask=cols<N ...
WITH THE# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.import torchimport tritonimport triton.language as tl@triton.autotune( configs=[ triton.Config({ 'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'NUM_SM'...
其形状为[group_size,3]# dim0is group_size,dim1is the valuesof<M,N,K>ofeach gemm #第0维是 group_size,第1维是每个GEMM的<M,N,K>值 group_gemm_sizes,# device tensorofleading dimension sizes.its shape is[group_size,3]# 设备张量的主导维度大小。其形状为[group_size,3]# dim0is ...
Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 16, 'GROUP_SIZE_M': 1, 'waves_per_eu': 2}, num_warps=4, num_stages=0), triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 16, 'GROUP_SIZE_M': 4, 'waves_per_eu': 2}...