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add fp16 support

This commit is contained in:
Dun Liang 2022-03-15 17:45:39 +08:00
parent 7cf6165a10
commit 39ecdd84fd
49 changed files with 2124 additions and 290 deletions
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58
python/jittor/__init__.py
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@ -9,7 +9,7 @@
# file 'LICENSE.txt', which is part of this source code package.
# ***************************************************************
__version__ = '1.3.1.38'
__version__ = '1.3.1.38.1'
from jittor_utils import lock
with lock.lock_scope():
ori_int = int
@ -304,15 +304,52 @@ Var.cast = Var.cast
def array(data, dtype=None):
if isinstance(data, core.Var):
if dtype is None:
return data.clone()
return cast(data, dtype)
if dtype is not None:
ret = data.clone()
else:
ret = cast(data, dtype)
elif dtype is not None:
if isinstance(dtype, NanoString):
dtype = str(dtype)
elif callable(dtype):
dtype = dtype.__name__
return ops.array(np.array(data, dtype))
return ops.array(data)
ret = ops.array(np.array(data, dtype))
else:
ret = ops.array(data)
# TODO: move those code to core
amp_reg = jt.flags.amp_reg
if amp_reg and ret.numel() != 1 and ret.dtype.is_float():
if amp_reg & 16:
if amp_reg & 1:
if ret.dtype != "float32":
return ret.float32()
elif amp_reg & 2:
if ret.dtype != "float16":
return ret.float16()
return ret
def random(shape, dtype="float32", type="uniform"):
# TODO: move those code to core
if dtype == "float16":
# TODO: make curand support fp16
ret = ops.random(shape, "float32", type).float16()
else:
ret = ops.random(shape, dtype, type)
amp_reg = jt.flags.amp_reg
if amp_reg:
if amp_reg & 16:
if amp_reg & 1:
if ret.dtype != "float32":
return ret.float32()
elif amp_reg & 2:
if ret.dtype != "float16":
return ret.float16()
return ret
def float_auto(x):
if jt.flags.amp_reg & 2:
return x.float16()
return x.float32()
Var.float_auto = float_auto
def array64(data, dtype=None):
with jt.flag_scope(auto_convert_64_to_32=0):
@ -1419,18 +1456,15 @@ Var.size = size
def to_int(v):
dtype = str(v.dtype)
assert dtype.startswith("int")
assert v.dtype.is_int()
return v.item()
def to_float(v):
dtype = str(v.dtype)
assert dtype.startswith("float")
assert v.dtype.is_float()
return v.item()
def to_bool(v):
dtype = str(v.dtype)
assert dtype.startswith("int") or dtype=="bool"
assert v.dtype.is_int() or v.dtype.is_bool()
return ori_bool(v.item())
Var.__int__ = to_int

15
python/jittor/compile_extern.py
View File

@ -210,6 +210,12 @@ def setup_cuda_extern():
LOG.w(f"CUDA found but cub is not loaded:\n{line}")
libs = ["cublas", "cudnn", "curand"]
# in cuda 11.4, module memory comsumptions:
# default context: 259 MB
# cublas: 340 MB
# cudnn: 340 MB
if int(os.environ.get("conv_opt", "0")):
libs = ["cublas", "curand"]
for lib_name in libs:
try:
setup_cuda_lib(lib_name, extra_flags=link_cuda_extern)
@ -309,7 +315,11 @@ def install_cutt(root_folder):
if md5 != true_md5:
os.remove(fullname)
shutil.rmtree(dirname)
if not os.path.isfile(os.path.join(cache_path, "libcutt"+so)):
CUTT_PATH = os.environ.get("CUTT_PATH", "")
if not os.path.isfile(os.path.join(cache_path, "libcutt"+so)) or CUTT_PATH:
if CUTT_PATH:
dirname = CUTT_PATH
else:
LOG.i("Downloading cutt...")
download_url_to_local(url, filename, root_folder, true_md5)
@ -324,7 +334,8 @@ def install_cutt(root_folder):
zf.close()
LOG.i("installing cutt...")
arch_flag = ""
# -Xptxas -dlcm=ca actually not work
arch_flag = " -Xptxas -dlcm=ca "
if len(flags.cuda_archs):
arch_flag = f" -arch=compute_{min(flags.cuda_archs)} "
arch_flag += ''.join(map(lambda x:f' -code=sm_{x} ', flags.cuda_archs))

4
python/jittor/extern/cuda/cublas/inc/cublas_wrapper.h vendored
View File

@ -23,8 +23,8 @@ EXTERN_LIB cublasHandle_t cublas_handle;
static inline cudaDataType get_dtype(NanoString dtype) {
if (dtype == ns_float32) return CUDA_R_32F;
// if (dtype == ns_float64) return CUDA_R_64F;
// if (dtype == ns_float16) return CUDA_R_16F;
if (dtype == ns_float64) return CUDA_R_64F;
if (dtype == ns_float16) return CUDA_R_16F;
LOGf << "not support type" << dtype;
return CUDA_R_32F;
}

4
python/jittor/extern/cuda/cublas/ops/cublas_batched_matmul_op.cc vendored
View File

@ -124,6 +124,10 @@ void CublasBatchedMatmulOp::jit_run() {
if (use_tensorcore) {
computeType = CUBLAS_COMPUTE_32F_FAST_16F;
}
if (a->dtype() == ns_float16
|| b->dtype() == ns_float16 || c->dtype() == ns_float16) {
computeType = CUBLAS_COMPUTE_16F;
}
checkCudaErrors(cublasGemmStridedBatchedEx(handle_,
CUBLAS_OP_@Trans_b, CUBLAS_OP_@Trans_a,
k, n, m, &alpha,

4
python/jittor/extern/cuda/cublas/ops/cublas_matmul_op.cc vendored
View File

@ -81,6 +81,10 @@ void CublasMatmulOp::jit_run() {
if (use_tensorcore) {
computeType = CUBLAS_COMPUTE_32F_FAST_16F;
}
if (a->dtype() == ns_float16
|| b->dtype() == ns_float16 || c->dtype() == ns_float16) {
computeType = CUBLAS_COMPUTE_16F;
}
checkCudaErrors(cublasGemmEx(handle_,
CUBLAS_OP_@Trans_b, CUBLAS_OP_@Trans_a,
k, n, m, &alpha,

5
python/jittor/extern/cuda/cudnn/ops/cudnn_conv_op.cc vendored
View File

@ -175,6 +175,11 @@ void CudnnConvOp::jit_run() {
checkCudaErrors( cudnnSetConvolutionMathType(cudnnConvDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION) );
}
if (x->dtype() == ns_float16
|| y->dtype() == ns_float16 || w->dtype() == ns_float16) {
checkCudaErrors( cudnnSetConvolutionMathType(cudnnConvDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION) );
}
int dimY[] = {
(int)y->shape[findc("@YFORMAT", 'a')], // n
(int)y->shape[findc("@YFORMAT", 'b')], // c

11
python/jittor/misc.py
View File

@ -488,18 +488,11 @@ def arctan2(y,x):
angle = jt.zeros(x.shape,dtype=x.dtype)
x = (x!=0.0).ternary(x, x+1e-30)
angle = (y/x).arctan()
mask = (y<0) & (x<0)
if angle[mask].numel()>0:
angle[mask] -= np.pi
mask = (y>=0) &(x<0)
if angle[mask].numel()>0:
angle[mask] +=np.pi
mask = y<0 | ((y==0) & (x<0))
angle = angle + mask*np.pi
return angle
def nonzero(x):
r'''
Return the index of the elements of input tensor which are not equal to zero.

2
python/jittor/models/resnet.py
View File

@ -143,7 +143,7 @@ class ResNet(nn.Module):
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = self.avgpool(x).float_auto()
x = jt.reshape(x, (x.shape[0], -1))
x = self.fc(x)
return x

24
python/jittor/nn.py
View File

@ -37,6 +37,7 @@ def matmul_transpose(a, b):
assert len(a.shape) == 2 and len(b.shape) == 2
shape = list(a.shape)[:-1] + list(b.shape)
with jt.flag_scope(amp_reg = jt.flags.amp_reg | 4):
a = a.broadcast(shape, [len(shape)-2])
b = b.broadcast(shape)
return (a*b).sum(len(shape)-1)
@ -108,6 +109,7 @@ Example::
c = jt.matmul(a, b)
assert c.shape == [8, 10, 3, 5]
'''
with jt.flag_scope(amp_reg = jt.flags.amp_reg | 4):
len_a = len(a.shape)
len_b = len(b.shape)
if len_b == 1:
@ -488,22 +490,22 @@ class BCEWithLogitsLoss(Module):
def execute(self, output, target):
return binary_cross_entropy_with_logits(output,target,self.weight,self.pos_weight,self.size_average)
def softmax(x, dim = None):
def softmax(x, dim=None, log=False):
import jittor.other.code_softmax as code_softmax
if code_softmax.can_softmax_v1(x, dim):
return code_softmax.softmax_v1(x)
return code_softmax.softmax_v1(x, log)
if dim is None:
x = (x - x.max()).exp()
ret = x / x.sum()
else:
x = (x-x.max(dim, keepdims=True)).exp()
ret = x / x.sum(dim, keepdims=True)
if log: return ret.log()
return ret
jt.Var.softmax = softmax
def log_softmax(x,dim=None):
x = softmax(x,dim=dim)
return jt.log(x)
return softmax(x,dim=dim, log=True)
jt.Var.log_softmax = log_softmax
def log_sigmoid(x):
@ -832,6 +834,7 @@ class Conv(Module):
oh = (H+self.padding[0]*2-Kh*self.dilation[0]+self.dilation[0]-1)//self.stride[0]+1
ow = (W+self.padding[1]*2-Kw*self.dilation[1]+self.dilation[1]-1)//self.stride[1]+1
assert oh>0 and ow>0
with jt.flag_scope(amp_reg = jt.flags.amp_reg | 4):
xx = x.reindex([N,self.out_channels,C,oh,ow,Kh,Kw], [
'i0', # Nid
'i2', # Cid
@ -1008,6 +1011,18 @@ class Conv3d(Module):
def execute(self, x):
return conv3d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
class Conv1d_sp(Linear):
def __init__(self, inchannels, outchannels, kernel_size=1, bias=True):
super().__init__(inchannels, outchannels, bias=bias)
assert kernel_size == 1
def execute(self, x):
x = x.transpose(0, 2, 1)
x = super().execute(x)
x = x.transpose(0, 2, 1)
return x
def conv2d(x, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
''' Applies a 2D convolution over an input signal composed of several input planes.
@ -1048,6 +1063,7 @@ def conv2d(x, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
Kh, Kw = weight.shape[-2:]
oh = (H+padding[0]*2-Kh*dilation[0]+dilation[0]-1)//stride[0]+1
ow = (W+padding[1]*2-Kw*dilation[1]+dilation[1]-1)//stride[1]+1
with jt.flag_scope(amp_reg = jt.flags.amp_reg | 4):
xx = x.reindex([N,out_channels,C,oh,ow,Kh,Kw], [
'i0', # Nid
'i2', # Cid

73
python/jittor/other/code_softmax.py
View File

@ -10,32 +10,48 @@ def can_softmax_v1(a, dim):
return False
return True
def softmax_v1(a):
def softmax_v1(a, log=False):
assert can_softmax_v1(a, -1)
length = a.shape[-1]
# tnum = 1024
tnum = 500 if length % 500 == 0 else 512
tnum = 125 if length % 125 == 0 else 128
# tnum = 125
# tnum = 1000 if length % 1000 == 0 else 1024
# tnum = 250
per_thread = (length-1) // tnum + 1
ILP = 1
for ilp in [8,4,2]:
if length % tnum == 0 and per_thread % ilp == 0:
ILP = ilp
per_thread //= ILP
break
for_loop = f"""
#pragma unroll
for (int i=0; i<{per_thread}; i++)
"""
if length % tnum == 0:
for_loop += f"if (i*{tnum}+threadIdx.x < len)\n"
if length % tnum != 0:
for_loop += f"if ((i*{tnum}+threadIdx.x)*{ILP} < len)\n"
return jt.code(a.shape, a.dtype, [a], cuda_header=f'''
#include <{jt.compile_extern.cub_home}cub/cub.cuh>
#include <type/fp16_compute.h>
''', cuda_src=f'''
__global__ void kernel(in0_type* x, out0_type* y, int len) {{
typedef cub::BlockReduce<float, {tnum}> BlockReduce;
__shared__ typename BlockReduce::TempStorage temp_storage;
int id = blockIdx.x * len;
in0_type v[{per_thread}];
{for_loop} v[i] = x[id+i*{tnum}+threadIdx.x];
float v1 = v[0];
{for_loop} v1 = max(v1, v[i]);
in0_type v[{per_thread}][{ILP}];
{for_loop}
vload<sizeof(in0_type)*{ILP}>(v[i], &x[id+(i*{tnum}+threadIdx.x)*{ILP}]);
// v[i] = x[id+i*{tnum}+threadIdx.x];
float v1 = -1e30;
{for_loop}
#pragma unroll
for (int j=0; j<{ILP}; j++) {{
v1 = max(v1, float(v[i][j]));
}}
__shared__ float vmax;
auto tmp = BlockReduce(temp_storage).Reduce(v1, cub::Max());
if (threadIdx.x == 0)
@ -43,9 +59,11 @@ __global__ void kernel(in0_type* x, out0_type* y, int len) {{
__syncthreads();
v1 = 0;
{for_loop} {{
v[i] = expf(v[i] - vmax);
v1 += v[i];
{for_loop}
#pragma unroll
for (int j=0; j<{ILP}; j++) {{
v[i][j] = expf(float(v[i][j]) - vmax);
v1 += float(v[i][j]);
}}
tmp = BlockReduce(temp_storage).Sum(v1);
@ -54,7 +72,15 @@ __global__ void kernel(in0_type* x, out0_type* y, int len) {{
vsum = tmp;
__syncthreads();
{for_loop} y[id+i*{tnum}+threadIdx.x] = v[i] / vsum;
{for_loop}
#pragma unroll
for (int j=0; j<{ILP}; j++)
v[i][j] = {
"@expand_op(log,@in0_type,float(v[i][j])/vsum)" if log
else "float(v[i][j])/vsum"
};
{for_loop}
vload<sizeof(in0_type)*{ILP}>(&y[id+(i*{tnum}+threadIdx.x)*{ILP}], v[i]);
}}
int len = in0->shape[in0->shape.size()-1];
int bnum = in0->numel() / len;
@ -64,15 +90,17 @@ CHECK(0 == cudaGetLastError());
''', cuda_grad_src=[f"""
__global__ void kernel(pout0_type* x, dout_type* y, out0_type* z, int len) {{
int id = blockIdx.x * len;
in0_type vx[{per_thread}];
in0_type vy[{per_thread}];
in0_type vx[{per_thread}][{ILP}];
in0_type vy[{per_thread}][{ILP}];
{for_loop} {{
vx[i] = x[id+i*{tnum}+threadIdx.x];
vy[i] = y[id+i*{tnum}+threadIdx.x];
vload<sizeof(in0_type)*{ILP}>(vx[i], &x[id+(i*{tnum}+threadIdx.x)*{ILP}]);
vload<sizeof(in0_type)*{ILP}>(vy[i], &y[id+(i*{tnum}+threadIdx.x)*{ILP}]);
}}
float v1 = 0;
{for_loop} v1 += vx[i]*vy[i];
{for_loop}
#pragma unroll
for (int j=0; j<{ILP}; j++)
v1 += {"vy[i][j];" if log else "vx[i][j]*vy[i][j];"}
typedef cub::BlockReduce<float, {tnum}> BlockReduce;
__shared__ typename BlockReduce::TempStorage temp_storage;
@ -83,7 +111,16 @@ __global__ void kernel(pout0_type* x, dout_type* y, out0_type* z, int len) {{
__syncthreads();
{for_loop}
z[id+i*{tnum}+threadIdx.x] = vx[i] * (vy[i] - reduce_var);
#pragma unroll
for (int j=0; j<{ILP}; j++)
vx[i][j] = {
"vy[i][j] - expf(vx[i][j]) * reduce_var;" if log
else "vx[i][j] * (vy[i][j] - reduce_var);"
}
{for_loop}
vload<sizeof(in0_type)*{ILP}>(&z[id+(i*{tnum}+threadIdx.x)*{ILP}],
vx[i]);
}}
int len = in0->shape[in0->shape.size()-1];
int bnum = in0->numel() / len;

20
python/jittor/pool.py
View File

@ -120,8 +120,8 @@ class Pool(Module):
for (int i2 = p2; i2 < out_shape2; i2 += s2)
{{ {forward_body} }}
}}
int tx = min(1024, out_shape3);
int ty = min(1024 / tx, out_shape2);
int tx = std::min(1024, out_shape3);
int ty = std::min(1024 / tx, out_shape2);
int bx = (out_shape2 - 1) / ty + 1;
int by = out_shape1;
int bz = out_shape0;
@ -143,8 +143,8 @@ class Pool(Module):
{{ {backward_body} }}
}}
cudaMemsetAsync(out_p, 0, out->size);
int tx = min(1024, pout_shape3);
int ty = min(1024 / tx, pout_shape2);
int tx = std::min(1024, pout_shape3);
int ty = std::min(1024 / tx, pout_shape2);
int bx = (pout_shape2 - 1) / ty + 1;
int by = pout_shape1;
int bz = pout_shape0;
@ -310,9 +310,9 @@ class Pool3d(Module):
for (int i2 = p2; i2 < out_shape2; i2 += s2)
{{ {forward_body} }}
}}
int tx = min(1024, out_shape4);
int ty = min(1024 / tx, out_shape3);
int tz = min(1024 / tx / ty, out_shape2);
int tx = std::min(1024, out_shape4);
int ty = std::min(1024 / tx, out_shape3);
int tz = std::min(1024 / tx / ty, out_shape2);
int bx = (out_shape2 - 1) / tz + 1;
int by = out_shape1;
int bz = out_shape0;
@ -337,9 +337,9 @@ class Pool3d(Module):
{{ {backward_body} }}
}}
cudaMemsetAsync(out_p, 0, out->size);
int tx = min(1024, pout_shape4);
int ty = min(1024 / tx, pout_shape3);
int tz = min(1024 / tx / ty, pout_shape2);
int tx = std::min(1024, pout_shape4);
int ty = std::min(1024 / tx, pout_shape3);
int tz = std::min(1024 / tx / ty, pout_shape2);
int bx = (pout_shape2 - 1) / tz + 1;
int by = pout_shape1;
int bz = pout_shape0;

16
python/jittor/src/grad.cc
View File

@ -39,11 +39,24 @@ template<class T> struct StackIniter {
#define STACK_ALLOC2(T, a, n) T a[n]
#endif
struct AmpGradGuard {
int amp_reg_bk;
AmpGradGuard(Op* op) {
amp_reg_bk = amp_reg;
amp_reg |= (op->flags.flags >> NodeFlags::_prefer_32);
}
~AmpGradGuard() {
amp_reg = amp_reg_bk;
}
};
VarPtr make_grad(Op* op, Var* out, Var* dout, Var* x, int x_index) {
if (dout == nullptr) return nullptr;
if (x_index<0) return nullptr;
LOGvvvv << "Make grad op:" >> op->name() << "inputs:" >> op->inputs()
<< "out:" >> out << "dout:" >> dout << "x:" >> x << "xid:" >> x_index;
AmpGradGuard agg(op);
auto dx = op->grad(out, dout, x, x_index);
if (x->loop_options)
dx->loop_options = x->loop_options;
@ -182,7 +195,10 @@ vector<VarPtr> grad(Var* loss, vector<Var*> targets) {
douts[i] = nullptr;
}
trace_grad_op = op;
{
AmpGradGuard agg(op);
op->grads(douts, dins);
}
// dump "for (Var* in : op->inputs())"
for (int i=0; i<n_i; i++,j++) {
auto id = id_buffer[j].second;

2
python/jittor/src/jit_key.h
View File

@ -167,7 +167,7 @@ inline JK& operator<<(JK& jk, int64 c) {
}
#ifdef __linux__
inline JK& operator<<(JK& jk, long long int c) {
inline JK& operator<<(JK& jk, int64_t c) {
return jk << (int64)c;
}
#endif

3
python/jittor/src/misc/deleter.h
View File

@ -13,7 +13,8 @@ namespace jittor {
struct Deleter {
std::function<void()> del;
inline Deleter(std::function<void()>&& func) : del(move(func)) {}
inline ~Deleter() { del(); }
inline Deleter() {}
inline ~Deleter() { if (del) del(); }
};
} // jittor

35
python/jittor/src/misc/nano_string.cc
View File

@ -9,6 +9,17 @@
namespace jittor {
DEFINE_FLAG(int, amp_reg, 0, "Auto mixed-precision control registers, bit 0: prefer 32; bit 1: prefer 16; bit 2: keep reduce type; bit 3 keep white list type; bit 4: array like op prefer too");
DEFINE_FLAG_WITH_SETTER(int, auto_mixed_precision_level, 0, "Auto mixed-precision optimization level, 0: not use fp16, 1-3: preserve level, not use fp16 for now; 4: perfer fp16, but some ops use fp32 e.g. sum,exp; 5: simular with 4, and array op will automatically convert to fp16; 6: all ops prefer fp16");
void setter_auto_mixed_precision_level(int value) {
if (value <= 3) amp_reg = 0; else
if (value == 4) amp_reg = amp_prefer16; else
if (value == 5) amp_reg = amp_prefer16 | amp_array_prefer; else
if (value == 6) amp_reg = amp_prefer16 | amp_array_prefer | amp_keep_reduce | amp_keep_white;
}
#define FOR_ALL_TYPES(m) \
m(bool) \
m(int8) \
@ -89,15 +100,18 @@ static unordered_set<string> unary_ops = {
"erfinv"
};
static unordered_set<string> unary_float_ops = {
static unordered_set<string> float_ops = {
"log",
"exp",
"sqrt",
"mean",
"divide",
};
static unordered_set<string> unary_int_ops = {
static unordered_set<string> int_ops = {
"round_int",
"floor_int",
"ceil_int",
"floor_divide",
};
static unordered_set<string> binary_ops = {
@ -127,6 +141,13 @@ static unordered_set<string> binary_ops = {
"mean",
};
static unordered_set<string> white_ops = {
// "log",
"exp",
"pow",
};
#define DEFINE_NS(T) NanoString ns_##T;
FOR_ALL_NS(DEFINE_NS);
@ -135,6 +156,9 @@ char __ns_to_string[ns_max_size*ns_max_len];
int __ns_len[ns_max_size];
static void init_ns() {
dsize_map["float16"] = 1;
is_float_map["float16"] = 1;
is_unsigned["float16"] = 0;
NanoString::ns_t i=0;
auto func = [&](const char* name, NanoString& ns) {
ns.set(NanoString::_index, i++, NanoString::_index_nbits);
@ -149,13 +173,16 @@ static void init_ns() {
if (unary_ops.count(name)) {
ns.set(NanoString::_type, NanoString::_unary, NanoString::_type_nbits);
ns.set(NanoString::_bool, is_bool.count(name));
ns.set(NanoString::_int, unary_int_ops.count(name));
ns.set(NanoString::_float, unary_float_ops.count(name));
ns.set(NanoString::_int, int_ops.count(name));
ns.set(NanoString::_float, float_ops.count(name));
} else
if (binary_ops.count(name)) {
ns.set(NanoString::_type, NanoString::_binary, NanoString::_type_nbits);
ns.set(NanoString::_bool, is_bool.count(name));
ns.set(NanoString::_int, int_ops.count(name));
ns.set(NanoString::_float, float_ops.count(name));
}
ns.set(NanoString::_white_list, white_ops.count(name));
__string_to_ns[name] = ns;
auto name2 = ns.to_cstring();
int len=0;

108
python/jittor/src/misc/nano_string.h
View File

@ -24,6 +24,7 @@ constexpr int ns_max_len = 16;
m(uint16) \
m(uint32) \
m(uint64) \
m(float16) \
m(float32) \
m(float64) \
\
@ -100,7 +101,7 @@ struct NanoString {
typedef uint16 ns_t;
enum Flags {
// bit0~7: index
_index=0, _index_nbits=8,
_index=0, _index_nbits=7,
_n=_index_nbits,
// bit0-1: type
@ -116,6 +117,8 @@ struct NanoString {
_float=_n+5,
// bit6-7: dsize(1,2,4,8 byte)
_dsize=_n+6, _dsize_nbits=2,
// bit8: white list
_white_list=_n+8,
};
ns_t data=0;
@ -130,11 +133,16 @@ struct NanoString {
inline ns_t index() const { return get(_index, _index_nbits); }
inline int len() const { return __ns_len[index()]; }
inline ns_t type() const { return get(_type, _type_nbits); }
inline ns_t is_bool() const { return get(_bool); }
inline ns_t is_int() const { return get(_int); }
inline ns_t is_unsigned() const { return get(_unsigned); }
inline ns_t is_float() const { return get(_float); }
// @pyjt(is_bool)
inline bool is_bool() const { return get(_bool); }
// @pyjt(is_int)
inline bool is_int() const { return get(_int); }
inline bool is_unsigned() const { return get(_unsigned); }
// @pyjt(is_float)
inline bool is_float() const { return get(_float); }
inline ns_t is_white() const { return get(_white_list); }
inline ns_t dsize() const { return 1<<get(_dsize, _dsize_nbits); }
inline ns_t dsize_() const { return get(_dsize, _dsize_nbits); }
inline ns_t is_dtype() const { return get(_type, _type_nbits)==_dtype; }
inline ns_t is_binary() const { return get(_type, _type_nbits)==_binary; }
inline ns_t is_unary() const { return get(_type, _type_nbits)==_unary; }
@ -156,28 +164,6 @@ struct NanoString {
{ return __ns_to_string+index()*ns_max_len; }
};
// force_type = 1 for int, 2 for float
inline
NanoString dtype_infer(NanoString v1, NanoString v2, int force_type=0, NanoString op=ns_void) {
bool is_float = v1.is_float() || v2.is_float();
int dsize = std::max(v1.dsize(), v2.dsize());
if (force_type == 1)
is_float = false;
else if (force_type == 2)
is_float = true;
if (is_float) {
if (dsize==4) return ns_float32;
return ns_float64;
} else {
if (dsize==8) return ns_int64;
if (dsize==4) return ns_int32;
if (dsize==2) return ns_int16;
if (op.data == ns_add.data || op.data == ns_subtract.data)
return ns_int8;
return v1;
}
}
// @pyjt(NanoString.__eq__)
inline bool eq(const NanoString& a, const NanoString& b) {
return a.data == b.data;
@ -199,4 +185,72 @@ inline std::ostream& operator<<(std::ostream& os, const NanoString& v) {
return os << v.to_cstring();
}
EXTERN_LIB int amp_reg;
constexpr int amp_prefer32 = 1;
constexpr int amp_prefer16 = 2;
constexpr int amp_keep_reduce = 4;
constexpr int amp_keep_white = 8;
constexpr int amp_array_prefer = 16;
inline NanoString float_dtype(int dsize_) {
if (amp_reg & amp_prefer32) return ns_float32;
if (amp_reg & amp_prefer16) return ns_float16;
return (dsize_ == 3) ? ns_float64 :
(dsize_ == 2 ) ? ns_float32 : ns_float16;
}
inline NanoString int_dtype(int dsize_) {
return (dsize_ == 3) ? ns_int64 :
(dsize_ == 2) ? ns_int32 :
(dsize_ == 1) ? ns_int16 : ns_int8;
}
inline NanoString dtype_infer(NanoString x, NanoString y) {
int dsize_ = std::max(x.dsize_(), y.dsize_());
bool is_float = x.is_float() || y.is_float();
if (is_float)
return float_dtype(dsize_);
else {
return int_dtype(dsize_);
}
}
inline NanoString binary_dtype_infer(NanoString op, NanoString x, NanoString y) {
if (op.is_bool()) return ns_bool;
int dsize_ = std::max(x.dsize_(), y.dsize_());
bool is_float = !op.is_int() &&
(x.is_float() || y.is_float() || op.is_float());
if (is_float) {
if (op.is_white() && !(amp_reg & amp_keep_white))
return (dsize_ == 3) ? ns_float64 : ns_float32;
return float_dtype(dsize_);
} else {
return int_dtype(dsize_);
}
}
inline NanoString unary_dtype_infer(NanoString op, NanoString x) {
if (op.is_bool()) return ns_bool;
int dsize_ = x.dsize_();
if (op.is_float()) {
if (op.is_white() && !(amp_reg & amp_keep_white))
return (dsize_ == 3) ? ns_float64 : ns_float32;
return float_dtype(dsize_);
}
if (op.is_int()) return int_dtype(dsize_);
return x;
}
inline NanoString reduce_dtype_infer(NanoString op, NanoString x) {
bool is_float = x.is_float() || op.is_float();
int dsize_ = x.dsize_();
if (is_float) {
if (amp_reg & amp_keep_reduce)
return float_dtype(dsize_);
return (dsize_ == 3) ? ns_float64 : ns_float32;
} else {
return x;
}
}
}

10
python/jittor/src/node.h
View File

@ -51,8 +51,14 @@ struct NodeFlags {
_grads=_n+6,
// bit7: has graph optimize
_has_gopt=_n+7,
// bit7: has vary input
// bit8: has vary input
_has_vary_input=_n+8,
// bit9: prefer 32 bit
_prefer_32=_n+9,
// bit10: force 16 bit
_prefer_16=_n+10,
// bit11: reduce keep type unchange
_reduce_keep=_n+11,
};
inline void set(Flags f, int a=1, int nbits=1) {
@ -90,7 +96,7 @@ struct Node {
operator Var*() { return (Var*)node; }
operator var_output_t() { return {(Op*)node, index}; }
};
static int64_t tflag_count;
static int64 tflag_count;
NodeFlags flags;
NanoString ns;
inline bool is_var() const { return flags.get(NodeFlags::_var); }

3
python/jittor/src/op.cc
View File

@ -25,11 +25,12 @@ DEFINE_FLAG(int, try_use_32bit_index, 0,
string_view_map<jit_op_entry_t> jit_ops;
string_view_map<string> jit_key_mapper;
int64_t Op::number_of_lived_ops = 0;
int64 Op::number_of_lived_ops = 0;
Op::Op() {
flags.set(NodeFlags::_var, 0);
flags.set(NodeFlags::_cpu, 1);
flags.flags |= ((amp_reg & 7) << NodeFlags::_prefer_32);
number_of_lived_ops++;
if (PREDICT_BRANCH_NOT_TAKEN(trace_py_var)) trace_data.record_node(this);
}

2
python/jittor/src/op.h
View File

@ -15,7 +15,7 @@ namespace jittor {
enum OpType {other=0, element=1, broadcast=2, reduce=3};
struct Op : Node {
vector<VarPtr> outputs_holder;
static int64_t number_of_lived_ops;
static int64 number_of_lived_ops;
inline Caster<Var*, Node::input_t> inputs() { CHECK_EXIST; return &_inputs; }
inline Caster<Var*, Node::output_t> outputs() { CHECK_EXIST; return &_outputs; }

2
python/jittor/src/op_compiler.cc
View File

@ -112,7 +112,7 @@ int OpCompiler::total_member_count() {
return member_count;
}
int64_t OpCompiler::eval(const string& expr, const unordered_map<string,string>& vars) {
int64 OpCompiler::eval(const string& expr, const unordered_map<string,string>& vars) {
if (expr.find("@") != string::npos) {
string new_expr;
for (size_t i=0; i<expr.size(); i++) {

10
python/jittor/src/ops/binary_op.cc
View File

@ -418,21 +418,13 @@ unordered_set<string> binary_ops = {
"bitwise_xor",
};
NanoString binary_dtype_infer(NanoString op, Var* x, Var* y) {
if (op == ns_mean) return dtype_infer(x->ns, y->ns, 2); // force float
int force_type=0;
if (op == ns_divide) force_type=2; // force float
if (op == ns_floor_divide) force_type=1; // force int
return op.is_bool() ? ns_bool : dtype_infer(x->ns, y->ns, force_type, op);
}
BinaryOp::BinaryOp(Var* x, Var* y, NanoString op) : x(x), y(y) {
flags.set(NodeFlags::_cpu);
flags.set(NodeFlags::_cuda);
set_type(OpType::element);
ns = op;
ASSERT(ns.is_binary());
z = create_output(nullptr, binary_dtype_infer(op, x, y));
z = create_output(nullptr, binary_dtype_infer(op, x->ns, y->ns));
}
VarPtr dirty_clone_broadcast(Var* v) {

2
python/jittor/src/ops/op_register.h
View File

@ -32,9 +32,11 @@ void op_registe(const OpInfo& op_info);
bool has_op(const string& name);
OpInfo get_op_info(const string& name);
struct OpCompiler;
struct OpByType {
unordered_set<string> types;
virtual string expand_op(const vector<string>& args) = 0;
virtual void post_pass(OpCompiler*) = 0;
};
extern vector<OpByType*> op_types;

8
python/jittor/src/ops/reduce_op.cc
View File

@ -271,7 +271,7 @@ ReduceOp::ReduceOp(Var* x, NanoString op, NanoVector dims, bool keepdims)
if (x->dtype() == ns_bool)
y = create_output(nullptr, ns_int32);
else
y = create_output(nullptr, binary_dtype_infer(ns, x, x));
y = create_output(nullptr, reduce_dtype_infer(ns, x->ns));
}
ReduceOp::ReduceOp(Var* x, NanoString op, uint dims_mask, uint keepdims_mask)
@ -283,7 +283,7 @@ ReduceOp::ReduceOp(Var* x, NanoString op, uint dims_mask, uint keepdims_mask)
ASSERT(ns.is_binary());
reduce_mask = dims_mask;
this->keepdims_mask = keepdims_mask;
y = create_output(nullptr, binary_dtype_infer(ns, x, x));
y = create_output(nullptr, reduce_dtype_infer(ns, x->ns));
}
ReduceOp::ReduceOp(Var* x, NanoString op, int dim, bool keepdims)
@ -359,8 +359,8 @@ void ReduceOp::jit_run() {
@for(i, DIM-2, -1, -1, auto ystride@i = ystride@{i+1} * yshape@{i+1};)
index_t xstride@{DIM-1} = 1;
@for(i, DIM-2, -1, -1, auto xstride@i = xstride@{i+1} * xshape@{i+1};)
Ty count = Ty(x->num) / Ty(y->num);
Ty rcount = Ty(y->num) / Ty(x->num);
Ty count = x->num*1.0 / y->num;
Ty rcount = y->num*1.0 / x->num;
@for(d, 0, DIM,@if(REDUCE>>d&1,, for (index_t xi@d=0; xi@d < xshape@d; xi@d++))) {
auto yid = 0 @for(d, 0, DIM,@if(REDUCE>>d&1,, + xi@d * ystride@d));
yp[yid] = @expand_op(init_@OP, @Ty);

2
python/jittor/src/ops/reindex_op.cc
View File

@ -132,7 +132,7 @@ void ReindexOp::jit_run() {
@for(d, 0, XDIM, index_t xid@d = @expand_macro(INDEX@d);)
auto xid = @for(d, 0, XDIM, + xid@d * xstride@d);
bool check_overflow = 0 @for(d, 0, XDIM, || xid@d<0 || xid@d>=xshape@d) @for(d, 0, OSIZE, || (@expand_macro(OFD@d)));
yp[yid] = check_overflow ? (@OVERFLOW) : xp[xid];
yp[yid] = check_overflow ? Tx(@OVERFLOW) : xp[xid];
}
}
#endif // JIT

6
python/jittor/src/ops/transpose_op.cc
View File

@ -28,6 +28,12 @@ TransposeOp::TransposeOp(Var* x, NanoVector axes_) : x(x), axes(axes_) {
for (int i=0; i<(int)xdim; i++)
axes.push_back(xdim-1-i);
}
if (axes.size() < xdim || (axes.size() == xdim && axes[xdim-1]==xdim-1)) {
static VarPtr(*fuse_transpose)(Var*, NanoVector) = get_op_info("fuse_transpose").get_constructor<VarPtr, Var*, NanoVector>();
auto var = fuse_transpose(x, axes);
forward(var);
return;
}
#ifdef HAS_CUDA
if (use_cuda) {
static VarPtr(*cutt_transpose)(Var*, NanoVector) = nullptr;

14
python/jittor/src/ops/unary_op.cc
View File

@ -32,6 +32,7 @@ static unordered_set<string> unary_ops = {
"uint16",
"uint32",
"uint64",
"float16",
"float32",
"float64",
// please keep float64 the last type
@ -533,22 +534,15 @@ UnaryOp::UnaryOp(Var* x, NanoString op) : x(x) {
ns = op;
ASSERT(ns.is_unary() | ns.is_dtype());
NanoString dtype;
if (ns.is_dtype()) {
if (ns == x->dtype()) {
forward(x);
return;
}
if (ns.is_dtype()) {
dtype = ns;
ns = ns_cast;
} else if (ns.is_bool())
dtype = ns_bool;
else if (ns.is_float())
dtype = dtype_infer(x->ns, x->ns, 2);
else if (ns.is_int())
dtype = dtype_infer(x->ns, x->ns, 1);
else {
dtype = x->ns;
}
} else
dtype = unary_dtype_infer(ns, x->ns);
y = create_output(nullptr, dtype);
}

8
python/jittor/src/opt/tuner/conv_tuner.cc
View File

@ -25,6 +25,7 @@
namespace jittor {
using namespace expr;
extern int use_cuda;
struct OpInspector {
// binary mask for
@ -229,9 +230,14 @@ void ConvTuner::forwardTune(FusedOp* fop) {
if (!(bop->y->input() && bop->x->input() && fop->has(bop->x->input()) && fop->has(bop->y->input()))) continue;
if (!(bop->x->input()->type()==OpType::broadcast && bop->y->input()->type()==OpType::broadcast)) return;
// only support float32 currently
// only support float32,float16 currently
if (use_cuda) {
if (bop->z->dtype() != ns_float32 && bop->z->dtype() != ns_float16)
continue;
} else {
if (bop->z->dtype() != ns_float32)
continue;
}
Op* ops[3] = {op, bop->x->input(), bop->y->input()};
int ok = 0;
LOGvvvv << "conv like op" << fop << fop->get_jit_key(get_jk());

2
python/jittor/src/profiler/profiler.cc
View File

@ -262,7 +262,7 @@ void Profiler::record_and_run(
Deleter _d;
if (is_fused) {
auto fop = ((FusedOp*)op);
if (fop->context && fop->context->entry) {
if (fop->context && fop->context->vrm.relay_groups.size()) {
// relay op
loop = rerun;
profiler.relay_extra_cost = 0;

6
python/jittor/src/pyjt/numpy.cc
View File

@ -21,7 +21,9 @@ NanoString npy2ns[] = {
ns_int64, ns_uint64,
ns_float32, ns_float64, ns_float64,
ns_void, ns_void, ns_void,
ns_void
ns_void, // 17
ns_void, ns_void, ns_void, ns_void, ns_void, // 22
ns_float16, // 23
};
NPY_TYPES ns2npy[] = {
@ -34,7 +36,7 @@ NPY_TYPES ns2npy[] = {
NPY_BYTE, NPY_SHORT, NPY_INT, NPY_LONGLONG,
NPY_UBYTE, NPY_USHORT, NPY_UINT, NPY_ULONGLONG,
#endif
NPY_FLOAT, NPY_DOUBLE
NPY_HALF, NPY_FLOAT, NPY_DOUBLE
};
void** PyArray_API;

6
python/jittor/src/pyjt/numpy.h
View File

@ -48,6 +48,8 @@ enum NPY_TYPES {
NPY_FLOAT, NPY_DOUBLE, NPY_LONGDOUBLE,
NPY_CFLOAT, NPY_CDOUBLE, NPY_CLONGDOUBLE,
NPY_OBJECT=17,
NPY_HALF=23,
NPY_END=24,
};
EXTERN_LIB NanoString npy2ns[];
@ -60,11 +62,11 @@ EXTERN_LIB NPY_TYPES ns2npy[];
inline bool is_c_style(PyArray_Proxy* obj) { return obj->flags & 1; }
inline NanoString get_type_str(PyArray_Proxy* obj) {
NanoString type = ns_void;
if (obj->descr->type_num < NPY_OBJECT)
if (obj->descr->type_num < NPY_END)
type = npy2ns[obj->descr->type_num];
CHECK(type != ns_void) << "Numpy type not support, type_num:"
<< obj->descr->type_num
<< "type_char:" << obj->descr->type;
<< "type_char:" << obj->descr->type << NPY_END << npy2ns[obj->descr->type_num];
return type;
}

2
python/jittor/src/pyjt/py_array_op.cc
View File

@ -141,7 +141,7 @@ ArrayOp::ArrayOp(PyObject* obj) {
} else {
// this is non-continue numpy array
#if defined(__linux__) || defined(_WIN32)
STACK_ALLOC(int64, dims, args.shape.size());
STACK_ALLOC(int64_t, dims, args.shape.size());
#elif defined(__APPLE__)
long dims[args.shape.size()];
#endif

4
python/jittor/src/pyjt/py_converter.h
View File

@ -274,7 +274,7 @@ DEF_IS(ArrayArgs, bool) is_type(PyObject* obj) {
DEF_IS(ArrayArgs, PyObject*) to_py_object(const T& a) {
#if defined(__linux__) || defined(_WIN32)
STACK_ALLOC(int64, dims, a.shape.size());
STACK_ALLOC(int64_t, dims, a.shape.size());
#elif defined(__APPLE__)
long dims[a.shape.size()];
#endif
@ -390,7 +390,7 @@ DEF_IS(VarHolder*, T) from_py_object(PyObject* obj, unique_ptr<VarHolder>& holde
struct DataView;
DEF_IS(DataView, PyObject*) to_py_object(T a) {
#if defined(__linux__) || defined(_WIN32)
STACK_ALLOC(int64, dims, a.shape.size());
STACK_ALLOC(int64_t, dims, a.shape.size());
#elif defined(__APPLE__)
long dims[a.shape.size()];
#endif

2
python/jittor/src/pyjt/py_ring_buffer.cc
View File

@ -110,7 +110,7 @@ static void push_py_object(RingBuffer* rb, PyObject* obj, uint64& __restrict__ o
rb->push(size, offset);
args.ptr = rb->get_ptr(size, offset);
#if defined(__linux__) || defined(_WIN32)
STACK_ALLOC(int64, dims, args.shape.size());
STACK_ALLOC(int64_t, dims, args.shape.size());
#elif defined(__APPLE__)
long dims[args.shape.size()];
#endif

52
python/jittor/src/type/common_op_type.cc
View File

@ -12,29 +12,7 @@ namespace jittor {
extern int use_cuda;
struct CommonOpType : OpByType {
CommonOpType() {
types = {
"bool",
"int8",
"int16",
"int32",
"int64",
"uint8",
"uint16",
"uint32",
"uint64",
"float32",
"float64",
};
}
string expand_op(const vector<string>& args) {
for (int i=1; i<args.size(); i+=2) {
if (!types.count(args[i]))
return "";
}
static unordered_map<string,string> cuda_map = {
unordered_map<string,string> common_op_type_cuda_map = {
{"logical_not", "(!($2))"},
{"bitwise_not", "(~($2))"},
{"negative", "(-($2))"},
@ -70,7 +48,31 @@ struct CommonOpType : OpByType {
{"mod", "@if(@strcmp($1,float32)==0,(($2)-::floorf(($2)/($4))*($4)),@if(@strcmp(@Tx,float64)==0,(($2)-::floor(($2)/($4))*($4)),(($2)%($4))))"},
{"init_maximum", "::numeric_min<$1>()"},
{"init_minimum", "::numeric_max<$1>()"},
};
struct CommonOpType : OpByType {
CommonOpType() {
types = {
"bool",
"int8",
"int16",
"int32",
"int64",
"uint8",
"uint16",
"uint32",
"uint64",
"float32",
"float64",
};
}
string expand_op(const vector<string>& args) {
for (int i=1; i<args.size(); i+=2) {
if (!types.count(args[i]))
return "";
}
auto& cuda_map = common_op_type_cuda_map;
static unordered_map<string,string> cpu_map = {
{"logical_not", "(!($2))"},
@ -151,6 +153,10 @@ struct CommonOpType : OpByType {
ret = cpu_map[args.at(0)];
return format(ret, args);
}
void post_pass(OpCompiler*) {
return;
}
};

164
python/jittor/src/type/fp16_compute.h Normal file
View File

@ -0,0 +1,164 @@
// ***************************************************************
// Copyright (c) 2021 Jittor. All Rights Reserved.
// Maintainers: Dun Liang <randonlang@gmail.com>.
// This file is subject to the terms and conditions defined in
// file 'LICENSE.txt', which is part of this source code package.
// ***************************************************************
#pragma once
#include "common.h"
#ifdef JIT_cuda
#include <driver_types.h>
#include <cuda_fp16.h>
namespace jittor {
typedef __half float16;
#if CUDA_ARCH >= 800
inline __device__ float16 max(float16 a, float16 b) { return __hmax(a, b); }
inline __device__ float16 min(float16 a, float16 b) { return __hmin(a, b); }
#else
inline __device__ float16 max(float16 a, float16 b) { return a<b?b:a; }
inline __device__ float16 min(float16 a, float16 b) { return a<b?a:b; }
#endif
inline __device__ float16 pow(float16 a, float16 b) { return ::pow(float32(a), float32(b)); }
template<int nbyte, class T>
__device__ inline void vload(T* __restrict__ a, T* __restrict__ b) {
if constexpr (nbyte<=0) return;
if constexpr (nbyte>=16) {
auto __restrict__ aa = (float4* __restrict__)a;
auto __restrict__ bb = (float4* __restrict__)b;
aa[0] = bb[0];
return vload<nbyte-16>(aa+1, bb+1);
}
if constexpr (nbyte>=8) {
auto __restrict__ aa = (float2* __restrict__)a;
auto __restrict__ bb = (float2* __restrict__)b;
aa[0] = bb[0];
return vload<nbyte-8>(aa+1, bb+1);
}
if constexpr (nbyte>=4) {
auto __restrict__ aa = (float* __restrict__)a;
auto __restrict__ bb = (float* __restrict__)b;
aa[0] = bb[0];
return vload<nbyte-4>(aa+1, bb+1);
}
if constexpr (nbyte>=2) {
auto __restrict__ aa = (__half* __restrict__)a;
auto __restrict__ bb = (__half* __restrict__)b;
aa[0] = bb[0];
return vload<nbyte-2>(aa+1, bb+1);
}
if constexpr (nbyte>=1) {
auto __restrict__ aa = (int8_t* __restrict__)a;
auto __restrict__ bb = (int8_t* __restrict__)b;
aa[0] = bb[0];
return vload<nbyte-1>(aa+1, bb+1);
}
}
}
using jittor::max;
using jittor::min;
using jittor::pow;
#else
namespace jittor {
struct float16 {
uint16 x;
inline float16(float32 f) {
unsigned x = *((int*)(void*)(&f));
unsigned u = (x & 0x7fffffff), remainder, shift, lsb, lsb_s1, lsb_m1;
unsigned sign, exponent, mantissa;
// Get rid of +NaN/-NaN case first.
if (u > 0x7f800000) {
this->x = 0x7fffU;
return;
}
sign = ((x >> 16) & 0x8000);
// Get rid of +Inf/-Inf, +0/-0.
if (u > 0x477fefff) {
this->x = sign | 0x7c00U;
return;
}
if (u < 0x33000001) {
this->x = sign | 0x0000U;
return;
}
exponent = ((u >> 23) & 0xff);
mantissa = (u & 0x7fffff);
if (exponent > 0x70) {
shift = 13;
exponent -= 0x70;
} else {
shift = 0x7e - exponent;
exponent = 0;
mantissa |= 0x800000;
}
lsb = (1 << shift);
lsb_s1 = (lsb >> 1);
lsb_m1 = (lsb - 1);
// Round to nearest even.
remainder = (mantissa & lsb_m1);
mantissa >>= shift;
if (remainder > lsb_s1 || (remainder == lsb_s1 && (mantissa & 0x1))) {
++mantissa;
if (!(mantissa & 0x3ff)) {
++exponent;
mantissa = 0;
}
}
this->x = (sign | (exponent << 10) | mantissa);
}
inline operator float() {
unsigned sign = ((x >> 15) & 1);
unsigned exponent = ((x >> 10) & 0x1f);
unsigned mantissa = ((x & 0x3ff) << 13);
if (exponent == 0x1f) { /* NaN or Inf */
mantissa = (mantissa ? (sign = 0, 0x7fffff) : 0);
exponent = 0xff;
} else if (!exponent) { /* Denorm or Zero */
if (mantissa) {
unsigned int msb;
exponent = 0x71;
do {
msb = (mantissa & 0x400000);
mantissa <<= 1; /* normalize */
--exponent;
} while (!msb);
mantissa &= 0x7fffff; /* 1.mantissa is implicit */
}
} else {
exponent += 0x70;
}
int temp = ((sign << 31) | (exponent << 23) | mantissa);
return reinterpret_cast<float&>(temp);
}
};
}
#endif

188
python/jittor/src/type/fp16_op_type.cc Normal file
View File

@ -0,0 +1,188 @@
// ***************************************************************
// Copyright (c) 2021 Jittor. All Rights Reserved.
// Maintainers: Dun Liang <randonlang@gmail.com>.
// This file is subject to the terms and conditions defined in
// file 'LICENSE.txt', which is part of this source code package.
// ***************************************************************
#include "common.h"
#include "utils/str_utils.h"
#include "ops/op_register.h"
#include "op_compiler.h"
namespace jittor {
extern int use_cuda;
extern unordered_map<string,string> common_op_type_cuda_map;
static bool isvar(char x) { return isalnum(x) || x == '_' || x == ':'; }
struct FP16OpType : OpByType {
FP16OpType() {
types = {
"float16",
};
}
string expand_op(const vector<string>& args) {
bool found_fp16 = 0;
for (int i=1; i<args.size(); i+=2) {
if (types.count(args[i]))
found_fp16 = 1;
}
if (!found_fp16) return "";
static unordered_map<string,string> cuda_map = {
{"logical_not", "(!($2))"},
{"bitwise_not", "(~($2))"},
{"negative", "(-($2))"},
{"abs", "::abs($2)"},
{"log", "::hlog(($1)($2))"},
{"exp", "::hexp(($1)($2))"},
{"sqrt", "::hsqrt(($1)($2))"},
{"round", "(($1) ::roundf(($2)))"},
{"floor", "(($1) ::floorf(($2)))"},
{"ceil", "(($1) ::ceilf(($2)))"},
{"round_int", "(($1) ::roundf(($2)))"},
{"floor_int", "(($1) ::floorf(($2)))"},
{"ceil_int", "(($1) ::ceilf(($2)))"},
{"sin", "(($1) ::sinf(($2)))"},
{"asin", "(($1) ::asinf(($2)))"},
{"sinh", "(($1) ::sinhf(($2)))"},
{"asinh", "(($1) ::asinhf(($2)))"},
{"cos", "(($1) ::cosf(($2)))"},
{"acos", "(($1) ::acosf(($2)))"},
{"cosh", "(($1) ::coshf(($2)))"},
{"acosh", "(($1) ::acoshf(($2)))"},
{"tan", "(($1) ::tanf(($2)))"},
{"atan", "(($1) ::atanf(($2)))"},
{"tanh", "(($1) ::tanhf(($2)))"},
{"atanh", "(($1) ::atanhf(($2)))"},
{"sigmoid", "(($1) (1.0f/(1.0f+::expf((::min($1(-($2)), $1(@if(@strcmp($1,float16)==0,30,300))))))))"},
{"erf", "(($1) ::erff(($2)))"},
{"erfinv", "(($1) ::erfinvf(($1)($2)))"},
{"cast", "(($1)($2))"},
{"pow", "::pow(($2),($4))"},
{"maximum", "::max($1($2), $1($4))"},
{"minimum", "::min($1($2), $1($4))"},
{"mod", "$1(($2)-::hfloor(($2)/($4))*($4))"},
{"init_maximum", "-32768.0f"},
{"init_minimum", "32768.0f"},
};
static unordered_map<string,string> cpu_map = {
{"logical_not", "(!($2))"},
{"bitwise_not", "(~($2))"},
{"negative", "(-($2))"},
{"abs", "std::abs($2)"},
{"log", "std::log(($1)($2))"},
{"exp", "std::exp(($1)($2))"},
{"sqrt", "std::sqrt(($1)($2))"},
{"round", "(($1)std::round(($2)))"},
{"floor", "(($1)std::floor(($2)))"},
{"ceil", "(($1)std::ceil(($2)))"},
{"round_int", "(($1)std::round(($2)))"},
{"floor_int", "(($1)std::floor(($2)))"},
{"ceil_int", "(($1)std::ceil(($2)))"},
{"sin", "(($1) std::sin(($2)))"},
{"asin", "(($1) std::asin(($2)))"},
{"sinh", "(($1) std::sinh(($2)))"},
{"asinh", "(($1) std::asinh(($2)))"},
{"cos", "(($1) std::cos(($2)))"},
{"acos", "(($1) std::acos(($2)))"},
{"cosh", "(($1) std::cosh(($2)))"},
{"acosh", "(($1) std::acosh(($2)))"},
{"tan", "(($1) std::tan(($2)))"},
{"atan", "(($1) std::atan(($2)))"},
{"tanh", "(($1) std::tanh(($2)))"},
{"atanh", "(($1) std::atanh(($2)))"},
{"sigmoid", "(($1) (1.0f/(1.0f+std::exp(std::min($1(-($2)), $1(@if(@strcmp($1,float32)==0,30,300)))))))"},
{"erf", "(($1) std::erf(($2)))"},
{"erfinv", "(jittor::_erfinv($2))"},
{"cast", "(($1)($2))"},
{"pow", "std::pow(($2),($4))"},
{"maximum", "std::max($1($2), $1($4))"},
{"minimum", "std::min($1($2), $1($4))"},
{"mod", "$1(($2)-std::floor(($2)/($4))*($4))"},
{"init_maximum", "-32768.0f"},
{"init_minimum", "32768.0f"},
};
static unordered_map<string,string> both_map {
{"add", "(($2)+($4))"},
{"subtract", "(($2)-($4))"},
{"multiply", "(($2)*($4))"},
{"divide", "($1(($1($2))/($1($4))))"},
{"floor_divide", "($1(($1($2))/($1($4))))"},
{"less", "(($2)<($4))"},
{"less_equal", "(($2)<=($4))"},
{"greater", "(($2)>($4))"},
{"greater_equal", "(($2)>=($4))"},
{"equal", "(($2)==($4))"},
{"not_equal", "(($2)!=($4))"},
{"left_shift", "(($2)<<($4))"},
{"right_shift", "(($2)>>($4))"},
{"logical_and", "(($2)&&($4))"},
{"logical_or", "(($2)||($4))"},
{"logical_xor", "((bool($2))!=(bool($4)))"},
{"bitwise_and", "(($2)&($4))"},
{"bitwise_or", "(($2)|($4))"},
{"bitwise_xor", "(($2)^($4))"},
{"mean", "(($2)+($4)*($1(rcount)))"},
{"init_add", "$1(0)"},
{"init_multiply", "$1(1)"},
{"init_logical_and", "true"},
{"init_logical_or", "false"},
{"init_logical_xor", "false"},
{"init_bitwise_and", "$1(-1)"},
{"init_bitwise_or", "$1(0)"},
{"init_bitwise_xor", "$1(0)"},
{"init_mean", "$1(0)"},
};
string ret;
if (both_map.count(args.at(0)))
ret = both_map[args.at(0)];
else if (use_cuda)
ret = cuda_map[args.at(0)];
else
ret = cpu_map[args.at(0)];
if (use_cuda) {
if (args[1] == "float32" && !both_map.count(args.at(0))) {
ret = common_op_type_cuda_map[args.at(0)];
}
if (args[1] == "float16" || args[1] == "float32") {
for (int i=3; i<args.size(); i+=2) {
if (args[i] != args[1]) {
ret = replace(ret, "$"+S(i-1),
args[1]+"($"+S(i-1)+")");
}
}
} else {
for (int i=3; i<args.size(); i+=2) {
if (args[i] != "float16") {
ret = replace(ret, "$"+S(i-1),
"float16($"+S(i-1)+")");
}
}
}
}
return format(ret, args);
}
void post_pass(OpCompiler* oc) {
string& src = oc->src;
if (src.find("float16") == string::npos)
return;
int i = src.rfind("#include");
if (i<0) i=0;
i = src.find('\n', i) + 1;
src = src.substr(0, i) + "#include \"type/fp16_compute.h\"\n" +
src.substr(i);
return;
}
};
static int _ = registe_op_type(new FP16OpType());
}

2
python/jittor/src/types.h
View File

@ -18,7 +18,7 @@ namespace jittor {
typedef int8_t int8;
typedef int16_t int16;
typedef int int32;
typedef int64_t int64;
typedef long long int64;
typedef uint8_t uint8;
typedef uint16_t uint16;
typedef uint32_t uint32;

4
python/jittor/src/var.cc
View File

@ -14,7 +14,7 @@
namespace jittor {
int64_t Var::number_of_lived_vars = 0;
int64 Var::number_of_lived_vars = 0;
DEFINE_FLAG(fast_shared_ptr<loop_options_t>, compile_options, {},
"Override the default loop transfrom options");
@ -42,7 +42,7 @@ string Var::to_string() {
return s;
}
int64_t Var::numel() {
int64 Var::numel() {
if (!shape.size()) return size=num=-1;
bool negtive = 0;
num=1;

6
python/jittor/src/var.h
View File

@ -18,13 +18,13 @@ struct Var : Node {
NanoVector shape;
cstr name;
fast_shared_ptr<loop_options_t> loop_options;
static int64_t number_of_lived_vars;
static int64 number_of_lived_vars;
// this var will be generated after alloc.
void* mem_ptr = nullptr;
Allocator* allocator = nullptr;
size_t allocation;
int64_t size, num;
int64 size, num;
inline bool is_float() const { CHECK_EXIST; return ns.is_float(); }
inline int dsize() const { CHECK_EXIST; return ns.dsize(); }
inline NanoString dtype() const { CHECK_EXIST; return ns; }
@ -40,7 +40,7 @@ struct Var : Node {
Var(NanoVector shape, NanoString dtype);
string to_string();
int64_t numel();
int64 numel();
void set_shape(NanoVector shape);
bool alloc(Allocator* allocator);
inline void share_with(Var* x, size_t offset = 0) { CHECK_EXIST; allocator = (Allocator*)x; allocation = offset; }

4
python/jittor/test/__main__.py
View File

@ -15,6 +15,7 @@ if __name__ == "__main__":
skip_l = int(os.environ.get("test_skip_l", "0"))
skip_r = int(os.environ.get("test_skip_r", "1000000"))
skip = os.environ.get("test_skip", "").split(",")
test_only = None
if "test_only" in os.environ:
test_only = set(os.environ.get("test_only").split(","))
@ -34,6 +35,9 @@ if __name__ == "__main__":
continue
if test_only and test_name not in test_only:
continue
for s in skip:
if s in test_name:
continue
print("Add Test", _, test_name)
suite.addTest(tests)

374
python/jittor/test/misc/superglue.py Normal file
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@ -0,0 +1,374 @@
from copy import deepcopy
from pathlib import Path
import jittor as jt
import jittor.nn as nn
import numpy as np
import os
split_size = 1000000
conv_opt = int(os.environ.get("conv_opt", "0"))
if conv_opt:
Conv1d_sp = nn.Conv1d_sp
else:
Conv1d_sp = nn.Conv1d
def MLP(channels: list, do_bn=True):
""" Multi-layer perceptron """
n = len(channels)
layers = []
for i in range(1, n):
layers.append(Conv1d_sp(channels[i - 1], channels[i], kernel_size=1, bias=True))
if i < (n - 1):
if do_bn:
layers.append(nn.BatchNorm(channels[i]))
# layers.append(nn.InstanceNorm1d(channels[i]))
# layers.append(nn.LayerNorm(channels[i]))
layers.append(nn.ReLU())
return nn.Sequential(*layers)
def normalize_keypoints(kpts, image_shape):
size = image_shape.flip(1) # shape=(b,2) ;h w -> w, h
center = size / 2
scaling = size.float32().max(1, keepdims=True) * 0.7
return (kpts - center[:, None, :]) / scaling[:, None, :]
class KeypointEncoder(nn.Module):
""" Joint encoding of visual appearance and location using MLPs"""
def __init__(self, feature_dim, layers, keypoint_position_dim=2):
super().__init__()
# self.keypoint_position_dim = keypoint_position_dim
self.encoder = MLP([keypoint_position_dim + 1] + layers + [feature_dim])
nn.init.constant_(self.encoder[-1].bias, 0.0)
def execute(self, kpts, scores):
inputs = jt.concat([kpts.t(), scores.unsqueeze(1)], dim=1)
return self.encoder(inputs)
cnt = 0
def attention(query, key, value):
global cnt
cnt += 1
b, d, h, n = query.shape
# print("attention", b,d,h,n, cnt)
dim_factor = (1.0 / d)**0.5
query = query.transpose(0, 2, 3, 1).reshape(b * h, -1, d) * dim_factor
key = key.transpose(0, 2, 1, 3).reshape(b * h, d, -1)
value = value.transpose(0, 2, 3, 1).reshape(b * h, -1, d)
# print("attention", query.shape, key.shape, value.shape)
data = []
for i in range(0, query.shape[0], split_size):
end = min(i + split_size, query.shape[0])
tmp1 = nn.bmm(query[i:end], key[i:end])
tmp2 = nn.softmax(tmp1, dim=-1)
tmp3 = nn.bmm(tmp2, value[i:end])
tmp3.sync()
data.append(tmp3)
tmp3 = jt.concat(data)
# for i in range(0, query.shape[0], split_size):
# end = min(i + split_size, query.shape[0])
# tmp1 = nn.bmm(query[:,i:end], key[:,i:end])
# tmp2 = nn.softmax(tmp1, dim=-1)
# tmp3 = nn.bmm(tmp2, value[:,i:end])
# tmp3.sync()
# data.append(tmp3)
# tmp3 = jt.concat(data, dim=1)
# tmp1 = nn.bmm(query, key)
# print(tmp1.shape)
# tmp2 = nn.softmax(tmp1, dim=-1)
# print(tmp2.shape)
# tmp3 = nn.bmm(tmp2, value)
# print(tmp3.shape)
return tmp3.reshape(b, h, -1, d).transpose(0, 3, 1, 2)
return nn.bmm(nn.softmax(nn.bmm(query, key), dim=-1), value).reshape(b, h, -1, d).transpose(0, 3, 1, 2)
class MultiHeadedAttention(nn.Module):
""" Multi-head attention to increase model expressivitiy """
def __init__(self, num_heads: int, d_model: int):
super().__init__()
assert d_model % num_heads == 0
self.dim = d_model // num_heads
self.num_heads = num_heads
self.merge = Conv1d_sp(d_model, d_model, kernel_size=1)
self.proj = nn.ModuleList([deepcopy(self.merge) for _ in range(3)])
def execute(self, query, key, value):
batch_dim = query.size(0)
query, key, value = [l(x).reshape(batch_dim, self.dim, self.num_heads, -1) for l, x in zip(self.proj, (query, key, value))]
x = attention(query, key, value)
# x = attention_chunk(query, key, value)
return self.merge(x.reshape(batch_dim, self.dim * self.num_heads, -1))
class AttentionalPropagation(nn.Module):
def __init__(self, feature_dim: int, num_heads: int):
super().__init__()
self.attn = MultiHeadedAttention(num_heads, feature_dim)
self.mlp = MLP([feature_dim * 2, feature_dim * 2, feature_dim])
nn.init.constant_(self.mlp[-1].bias, 0.0)
def execute(self, x, source):
message = self.attn(x, source, source)
return self.mlp(jt.concat([x, message], dim=1))
class AttentionalGNN(nn.Module):
def __init__(self, feature_dim: int, layer_names: list):
super().__init__()
self.layers = nn.ModuleList([AttentionalPropagation(feature_dim, 4) for _ in range(len(layer_names))])
self.is_cross = [x == 'cross' for x in layer_names]
def execute(self, desc0, desc1):
for layer, is_cross in zip(self.layers, self.is_cross):
layer.attn.prob = []
if is_cross:
src0, src1 = desc1, desc0
else: # if name == 'self':
src0, src1 = desc0, desc1
# delta0, delta1 = layer(desc0, src0), layer(desc1, src1)
delta0 = layer(desc0, src0)
# print(delta0.numel()*4)
# breakpoint()
jt.sync_all()
delta1 = layer(desc1, src1)
jt.sync_all()
desc0, desc1 = (desc0 + delta0), (desc1 + delta1)
jt.sync_all()
return desc0, desc1
def log_sinkhorn_iterations(Z, log_mu, log_nu, iters: int):
""" Perform Sinkhorn Normalization in Log-space for stability"""
u, v = jt.zeros_like(log_mu), jt.zeros_like(log_nu)
for _ in range(iters):
u = log_mu - (Z + v.unsqueeze(1)).exp().sum(dim=2).log()
v = log_nu - (Z + u.unsqueeze(2)).exp().sum(dim=1).log()
return Z + u.unsqueeze(2) + v.unsqueeze(1)
def log_optimal_transport(scores, alpha, iters: int):
""" Perform Differentiable Optimal Transport in Log-space for stability"""
b, m, n = scores.shape
ms, ns = jt.float(m, requires_grad=False), jt.float(n, requires_grad=False)
bins0 = alpha.broadcast([b, m, 1])
bins1 = alpha.broadcast([b, 1, n])
alpha = alpha.broadcast([b, 1, 1])
couplings = jt.concat([jt.concat([scores, bins0], -1), jt.concat([bins1, alpha], -1)], 1)
norm = -(ms + ns).log()
log_mu = jt.concat([norm.broadcast([m]), ns.log() + norm])
log_nu = jt.concat([norm.broadcast([n]), ms.log() + norm])
log_mu, log_nu = log_mu[None].broadcast([b, m + 1]), log_nu[None].broadcast([b, n + 1])
Z = log_sinkhorn_iterations(couplings, log_mu, log_nu, iters)
Z = Z - norm # multiply probabilities by M+N
return Z
def arange_like(x, dim: int):
return jt.ones(x.shape[dim], dtype=x.dtype)[None].cumsum()[0] - 1 # traceable in 1.1
default_config = {
'descriptor_dim': 256, # SuperPoint
'weights': 'indoor',
'keypoint_encoder': [32, 64, 128, 256], # SuperPoint
'GNN_layers': ['self', 'cross'] * 9,
'sinkhorn_iterations': 100,
'match_threshold': 0.2,
}
def get_weighted_loss_batch(scores, all_matches):
matches0, matches1 = all_matches.chunk(chunks=2, dim=2)
batchIdx = jt.arange(all_matches.shape[0]).unsqueeze(1).repeat(1, all_matches.shape[1])
batchIdx, matches0, matches1 = batchIdx.view(-1), matches0.view(-1), matches1.view(-1)
valid_index0, valid_index1 = matches0 >= 0, matches1 >= 0
valid_match = jt.logical_and(valid_index0, valid_index1)
valid_unmatch = jt.logical_xor(valid_index0, valid_index1)
num_match = valid_match.sum().maximum(1e-9)
num_unmatch = valid_unmatch.sum().maximum(1e-9)
score_ = scores[batchIdx, matches0, matches1]
score_match_ = (score_*valid_match).float32().sum() / num_match
score_umatch_ = (score_*valid_unmatch).float32().sum() / num_unmatch
return -(num_unmatch * score_match_ + num_match * score_umatch_) / (num_match + num_unmatch)
# print(score_umatch_, score_match_)
# return -(score_match + score_umatch) / (num_match + num_unmatch)
score_match = scores[(batchIdx[valid_match], matches0[valid_match], matches1[valid_match])].float32().mean() if num_match > 0 else 0
score_umatch = scores[(batchIdx[valid_unmatch], matches0[valid_unmatch], matches1[valid_unmatch])].float32().mean() if num_unmatch > 0 else 0
# print(score_match, score_umatch)
return -(num_unmatch * score_match + num_match * score_umatch) / (num_match + num_unmatch)
def add_dustbin(scores, alpha):
b, m, n = scores.shape
bins0 = jt.broadcast(alpha, (b, m, 1))
bins1 = jt.broadcast(alpha, (b, 1, n))
alpha = jt.broadcast(alpha, (b, 1, 1))
couplings = jt.concat([jt.concat([scores, bins0], -1), jt.concat([bins1, alpha], -1)], 1)
return couplings
class SuperGlue(nn.Module):
def __init__(self, config):
super().__init__()
config = {**default_config, **config}
self.descriptor_dim = config['descriptor_dim']
self.keypoint_encoder = config['keypoint_encoder']
self.GNN_layers = config['GNN_layers']
self.sinkhorn_iterations = config['sinkhorn_iterations']
self.match_threshold = config['match_threshold']
self.keypoint_position_dim = config['keypoint_position_dim']
self.use_dual_softmax = config['use_dual_softmax']
self.scale = jt.float(self.descriptor_dim**-0.5).stop_grad()
# self.scale.requires_grad = False
# self.des_extend = MLP([128, 256])
self.kenc = KeypointEncoder(self.descriptor_dim, self.keypoint_encoder, keypoint_position_dim=self.keypoint_position_dim)
self.gnn = AttentionalGNN(self.descriptor_dim, self.GNN_layers)
self.final_proj = Conv1d_sp(self.descriptor_dim, self.descriptor_dim, kernel_size=1, bias=True)
self.bin_score = jt.float(1.0)
def execute(self, data):
"""Run SuperGlue on a pair of keypoints and descriptors"""
kpts0, kpts1 = data['keypoints0'], data['keypoints1']
desc0, desc1 = data['descriptors0'], data['descriptors1']
all_matches = data['all_matches']
# match_num = data['match_num']
if kpts0.shape[1] == 0 or kpts1.shape[1] == 0 or all_matches.shape[1] == 0: # no keypoints or no matches/unmatches
shape0, shape1 = kpts0.shape[:-1], kpts1.shape[:-1]
return {
'matches0': jt.ones(shape0, dtype=jt.int),
'matches1': jt.ones(shape1, dtype=jt.int),
'matching_scores0': jt.zeros(shape0, dtype=jt.float),
'matching_scores1': jt.zeros(shape1, dtype=jt.float),
'skip_train': True
}
# Keypoint normalization.
kpts0 = normalize_keypoints(kpts0, data['shape0'])
kpts1 = normalize_keypoints(kpts1, data['shape1'])
# Keypoint MLP encoder.
# desc0 = self.des_extend(desc0) + self.kenc(kpts0, data['scores0'])
# desc1 = self.des_extend(desc1) + self.kenc(kpts1, data['scores1'])
desc0 = desc0 + self.kenc(kpts0, data['scores0'])
desc1 = desc1 + self.kenc(kpts1, data['scores1'])
# Multi-layer Transformer network.
desc0, desc1 = self.gnn(desc0, desc1)
# Final MLP projection.
desc0, desc1 = self.final_proj(desc0), self.final_proj(desc1)
desc0_t = desc0.t()
losses = []
for i in range(0, desc1.shape[0], split_size):
end = min(desc1.shape[0], i + split_size)
# Compute matching descriptor distance.
scores = nn.bmm(desc0_t[i:end], desc1[i:end]) * self.scale # 457.76 MB
scores.sync()
# Run the optimal transport.
if self.use_dual_softmax:
scores = add_dustbin(scores, self.bin_score) # 458.68 MB
scores.sync()
dual_softmax0, dual_softmax1 = nn.log_softmax(scores, 1), nn.log_softmax(scores, 2)
scores = dual_softmax0 + dual_softmax1 # 458.22 MB
scores.sync()
else:
scores = log_optimal_transport(scores, self.bin_score, iters=self.config['sinkhorn_iterations'])
# loss = torch.stack([get_match_score(scores[b], all_matches[b]) for b in range(all_matches.shape[0])])
loss = get_weighted_loss_batch(scores, all_matches[i:end])
loss.sync()
losses.append(loss)
loss = jt.concat(losses)
'''
# Compute matching descriptor distance.
scores = nn.bmm(desc0.t(), desc1) * self.scale # 457.76 MB
scores.sync()
# Run the optimal transport.
if self.use_dual_softmax:
scores = add_dustbin(scores, self.bin_score) # 458.68 MB
scores.sync()
dual_softmax0, dual_softmax1 = nn.log_softmax(scores, 1), nn.log_softmax(scores, 2)
scores = dual_softmax0 + dual_softmax1 # 458.22 MB
scores.sync()
else:
scores = log_optimal_transport(scores, self.bin_score, iters=self.config['sinkhorn_iterations'])
# loss = torch.stack([get_match_score(scores[b], all_matches[b]) for b in range(all_matches.shape[0])])
loss = get_weighted_loss_batch(scores, all_matches)
# print(scores.shape, all_matches.shape, loss.shape)
'''
# matches0, matches1 = all_matches.chunk(chunks=2, dim=2)
# batchIdx = jt.arange(0, b).unsqueeze(1).repeat(1, num)
# batchIdx, matches0, matches1 = batchIdx.view(-1), matches0.view(-1), matches1.view(-1)
# validmatch = (matches0 >= 0) | (matches1 >= 0)
# batchIdx, matches0, matches1 = batchIdx[validmatch], matches0[validmatch], matches1[validmatch]
# matches0[matches0 == -1] = n
# matches1[matches1 == -1] = m
# loss_mean = -scores[(batchIdx, matches0, matches1)].mean()
# loss_mean = nn.l1_loss(loss_mean, jt.float(0.0))
if not data['return_match']:
return {'loss': loss}
with jt.no_grad():
b, n, m = scores.shape
# Get the matches with score above "match_threshold".
indices0, max0 = scores[:, :-1, :-1].argmax(2)
indices1, max1 = scores[:, :-1, :-1].argmax(1)
mutual0 = jt.arange(0, n)[None] == indices1.gather(1, indices0)
mutual1 = jt.arange(0, m)[None] == indices0.gather(1, indices1)
# zero = scores.new_tensor(0)
# mscores0 = torch.where(mutual0, max0.values.exp(), zero)
mscores0 = max0.exp()
mscores0[mutual0.logical_not()] = 0
# mscores1 = torch.where(mutual1, mscores0.gather(1, indices1), zero)
mscores1 = mscores0.gather(1, indices1)
mscores1[mutual1.logical_not()] = 0
valid0 = mutual0 & (mscores0 > self.match_threshold)
valid1 = mutual1 & valid0.gather(1, indices1)
# indices0 = torch.where(valid0, indices0, indices0.new_tensor(-1))
# indices1 = torch.where(valid1, indices1, indices1.new_tensor(-1))
indices0[valid0.logical_not()] = -1
indices1[valid1.logical_not()] = -1
return {
'matches0': indices0, # use -1 for invalid match
'matches1': indices1, # use -1 for invalid match
'matching_scores0': mscores0,
'matching_scores1': mscores1,
'loss': loss,
}
# scores big value or small value means confidence? log can't take neg value

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@ -0,0 +1,344 @@
# ***************************************************************
# Copyright (c) 2021 Jittor. All Rights Reserved.
# Maintainers: Dun Liang <randonlang@gmail.com>.
# This file is subject to the terms and conditions defined in
# file 'LICENSE.txt', which is part of this source code package.
# ***************************************************************
import unittest
import jittor as jt
import numpy as np
import os
n = 400000000
# n = 4000000
n = 7680000
def get_mem_band():
a = jt.rand((n)).float32()
for i in range(100):
a.copy().sync()
jt.sync_all(True)
import time
t = time.time()
for i in range(1000):
a.copy().sync()
jt.sync_all(True)
dt = time.time() - t
band = a.numel() * 4 * 2000 / dt / 1024**3
print("Mem band: ", band)
return band
def check_simple_add_band():
# copy: 816
# S=1 128,1024, ILP=1 634
# S=0 128,1024, ILP=1 734
# S=0 128,512, ILP=1 716
# S=0 64,1024, ILP=1 706
# S=0 256,1024, ILP=1 706
def test(S=0, B=128, T=1024, ILP=1):
a = jt.rand((n)).float32()
jt.sync_all(True)
jt.flags.log_silent = 1
with jt.profile_scope(100, 1000) as rep:
b = jt.code(a.shape, a.dtype, [a],
cuda_header="#include \"type/fp16_compute.h\"",
cuda_src=f"""
__global__ void kernel(in0_type * __restrict__ a, in0_type* __restrict__ b, int num) {{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int tnum = blockDim.x * gridDim.x;
#define ILP {ILP}
for (int i=tid*ILP; i<num; i+=tnum*ILP) {{
// b[i] = a[i];
vload<ILP*sizeof(in0_type)>(b+i, a+i);
{"__syncthreads();" if S else ""}
}}
}}
kernel<<<{B},{T}>>>(in0_p, out0_p, in0->num);
""")
b.sync()
bw = float(rep[-1][9]) / 1024**3
s = f"S={S}, B={B}, T={T}, ILP={ILP} BW={bw}"
print(s)
return s, bw
def test2(S=0, B=128, T=1024, ILP=1):
a = jt.rand((n)).float32()
jt.sync_all(True)
# jt.flags.log_silent = 0
with jt.profile_scope(10, 1000) as rep:
b = jt.code(a.shape, a.dtype, [a],
cuda_header="#include \"type/fp16_compute.h\"",
cuda_src=f"""
__global__ void kernel(float2 * __restrict__ a, float2* __restrict__ b, int num) {{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int tnum = blockDim.x * gridDim.x;
#define ILP 1
for (int i=tid*ILP; i<num; i+=tnum*ILP) {{
b[i] = a[i];
// b[i+1] = a[i+1];
// vload<ILP*sizeof(in0_type)>(b+i, a+i);
{"__syncthreads();" if S else ""}
}}
}}
kernel<<<{B},{T}>>>((float2*)in0_p, (float2*)out0_p, in0->num/2);
""")
b.sync()
bw = float(rep[-1][9]) / 1024**3
s = f"T2: S={S}, B={B}, T={T}, ILP={ILP} BW={bw}"
print(s)
return s, bw
def test3(S=0, B=128, T=1024, ILP=1, C=0):
a = jt.rand((n)).float32()
b = jt.rand(B)
jt.sync_all(True)
jt.flags.log_silent = 1
with jt.profile_scope(100, 1000) as rep:
b = jt.code(a.shape, a.dtype, [a, b],
cuda_header="#include \"type/fp16_compute.h\"",
cuda_src=f"""
__global__ void kernel(in0_type * __restrict__ a, in0_type* __restrict__ b, int num) {{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int tnum = blockDim.x * gridDim.x;
#define ILP {ILP}
for (int i=tid*ILP; i<num; i+=tnum*ILP) {{
// b[i] = a[i];
vload<ILP*sizeof(in0_type)>(b+i, a+i);
{"__syncthreads();" if S else ""}
}}
{"__syncthreads();" if C else ""}
}}
kernel<<<in1->shape[0],{T}>>>(in0_p, out0_p, in0->num);
""")
b.compile_options = {"FLAGS: -Xptxas -dlcm=ca ": C}
# b.compile_options = {"FLAGS: –Xptxas –dlcm=ca ": 1}
b.sync()
bw = float(rep[-1][9]) / 1024**3
s = f"T3: S={S}, B={B}, T={T}, ILP={ILP} C={C} BW={bw}"
print(s)
return s, bw
def test4(S=0, B=128, T=1024, ILP=1, C=0, name="b.png"):
a = jt.rand((n)).float32()
b = jt.rand(B*4).uint32()
jt.sync_all(True)
# jt.flags.log_silent = 1
with jt.profile_scope(100, 10000) as rep:
_ = jt.code(a.shape, a.dtype, [a, b],
cuda_header="#include \"type/fp16_compute.h\"",
cuda_src=f"""
__device__ uint get_smid(void) {{
uint ret;
asm("mov.u32 %0, %smid;" : "=r"(ret) );
return ret;
}}
__device__ uint get_time(void) {{
uint ret;
asm volatile("mov.u32 %0, %%globaltimer_lo;" : "=r"(ret));
return ret;
}}
__global__ void kernel(in0_type * __restrict__ a, in0_type* __restrict__ b, int num, in1_type* __restrict__ c) {{
uint t = get_time();
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int tnum = blockDim.x * gridDim.x;
#define ILP {ILP}
for (int i=tid*ILP; i<num; i+=tnum*ILP) {{
// b[i] = a[i];
vload<ILP*sizeof(in0_type)>(b+i, a+i);
{"__syncthreads();" if S else ""}
}}
{"__syncthreads();" if C else ""}
if (threadIdx.x == 0)
((uint4* __restrict__)c)[blockIdx.x] =
uint4{{get_smid(), t, get_time(), 0}};
}}
kernel<<<in1->shape[0]/4,{T}>>>(in0_p, out0_p, in0->num, in1_p);
""")
_.compile_options = {"FLAGS: -Xptxas -dlcm=ca ": C}
# b.compile_options = {"FLAGS: –Xptxas –dlcm=ca ": 1}
_.sync()
bw = float(rep[-1][9]) / 1024**3
b = b.data.reshape(-1, 4)[:,:3]
mint = b[:,1].min()
b[:,1:] -= mint
smmax = int(b[:,0].max())
smmin = int(b[:,0].min())
maxt = b.max()
# print(b)
s = f"T4: S={S}, B={B}, T={T}, ILP={ILP} C={C} BW={bw:.3f} sm={smmin},{smmax} maxt={maxt}"
print(s)
import pylab as pl
pl.figure(figsize=(16,16))
texts = []
pret = np.zeros(200, dtype="uint32")
for i in range(B):
smid, s, t = b[i]
pl.plot([s,t], [smid, smid], 'ro-')
texts.append((s, smid, i))
texts.append((t, smid, i))
texts = sorted(texts)
for (s, smid, bid) in texts:
cpos = max(pret[smid], s)
pl.text(cpos, smid, str(bid))
pret[smid] = cpos + maxt // 30
# print("???")
# adjust_text(texts, arrowprops=dict(arrowstyle='->', color='blue'))
# print("???")
pl.savefig(name)
pl.close()
return s, bw
# test(S=0, B=128, T=1024, ILP=1)
# test(S=1, B=128, T=1024, ILP=1)
# test(S=0, B=64, T=1024, ILP=1)
# test(S=0, B=256, T=1024, ILP=1)
# test(S=1, B=128, T=512, ILP=1)
# test(S=1, B=128, T=256, ILP=1)
# test(S=0, B=128, T=1024, ILP=2)
# test(S=0, B=128, T=1024, ILP=4)
# test(S=0, B=128, T=512, ILP=2)
# test(S=0, B=128, T=512, ILP=4)
# test(S=1, B=128, T=1024, ILP=2)
# test(S=1, B=128, T=1024, ILP=4)
# test(S=1, B=128, T=1024, ILP=8)
# test(S=1, B=128, T=1024, ILP=16)
# test(S=1, B=128, T=512, ILP=2)
# test(S=1, B=128, T=512, ILP=4)
# test(S=1, B=256, T=1024, ILP=2)
# test(S=1, B=512, T=1024, ILP=2)
# test(S=1, B=256, T=1024, ILP=4)
# test(S=1, B=256, T=1024, ILP=8)
# test(S=1, B=256, T=1024, ILP=16)
# test(S=1, B=256, T=512, ILP=2)
# test(S=1, B=256, T=512, ILP=4)
# test(S=1, B=128, T=256, ILP=2)
# test(S=1, B=128, T=256, ILP=4)
# test(S=0, B=128, T=256, ILP=2)
# test(S=0, B=128, T=256, ILP=4)
# for b in [1, 2, 4, 8, 16, 32, 64, 128,256]:
# test(S=1, B=b, T=512, ILP=2)
import matplotlib as mpl
mpl.use('Agg')
import pylab as pl
import numpy as np
# test4(S=1, B=82, T=1024, ILP=2, C=0, name="b.png")
# test4(S=1, B=83, T=1024, ILP=2, C=0, name="c.png")
# test4(S=1, B=82*3, T=512, ILP=2, C=0, name="d1.png")
# test4(S=1, B=82*3+1, T=512, ILP=2, C=0, name="d2.png")
# test4(S=1, B=82*6+1, T=512, ILP=2, C=0, name="d3.png")
# test4(S=0, B=82*6+1, T=512, ILP=2, C=0, name="d4.png")
for b in range(70, 83):
test4(S=1, B=b, T=1024, ILP=2, C=0, name=f"b-{b}.png")
# data = []
# for b in range(32, 2000, 8):
# _, bw = test3(S=0, B=b, T=32, ILP=2)
# data.append([b, bw])
# data = np.array(data)
# pl.plot(data[:,0], data[:,1])
# for t in [32, 64, 128, 256, 512, 1024]:
# data = []
# for b in range(32, 2000, 8):
# _, bw = test3(S=1, B=b*(1024//t), T=t, ILP=2)
# data.append([b, bw])
# data = np.array(data)
# pl.plot(data[:,0], data[:,1])
# for t in [1024]:
# for c in [0,1]:
# data = []
# # for b in range(32, 1000, 8):
# for b in range(32, 33, 8):
# _, bw = test3(S=c, B=b*(1024//t), T=t, ILP=2, C=0)
# data.append([b, bw])
# data = np.array(data)
# pl.plot(data[:,0], data[:,1])
# for ilp in [2]:
# for s in [1]:
# for t in [1024,512,256,128]:
# data = []
# for b in range(32, 1100, 8):
# _, bw = test3(S=s, B=b*(1024//t), T=t, ILP=ilp)
# data.append([b, bw])
# data = np.array(data)
# pl.plot(data[:,0], data[:,1])
# pl.savefig("a.png")
# pl.close()
# for b in range(80, 90, 1):
# _, bw = test3(S=1, B=b, T=1024, ILP=2)
# # 82
# for b in range(240, 260, 1):
# _, bw = test3(S=1, B=b, T=512, ILP=2)
# # 82*3 = 246
# for b in range(240, 500, 1):
# _, bw = test3(S=1, B=b, T=256, ILP=2)
# # 492 = 82*6
# for b in range(240, 1000, 1):
# _, bw = test3(S=1, B=b, T=128, ILP=2)
# # 984 = 82*12
# for b in [128,256]:
# test(S=1, B=b, T=1024, ILP=2)
# for b in [128,256]:
# test(S=0, B=b, T=512, ILP=2)
# for b in [128,256]:
# test(S=0, B=b, T=1024, ILP=2)
# for b in [128,256]:
# test(S=1, B=b, T=512, ILP=1)
# for b in [128,256]:
# test(S=1, B=b, T=1024, ILP=1)
# for b in [128,256]:
# test(S=0, B=b, T=512, ILP=1)
# for b in [128,256]:
# test(S=0, B=b, T=1024, ILP=1)
# test(S=1, B=128, T=512, ILP=4)
# test(S=1, B=64, T=512, ILP=2)
# test(S=1, B=80, T=512, ILP=2)
# test(S=1, B=100, T=512, ILP=2)
# test(S=1, B=110, T=512, ILP=2)
# test(S=1, B=115, T=512, ILP=2)
# test(S=1, B=120, T=512, ILP=2)
# test(S=1, B=130, T=512, ILP=2)
# test(S=1, B=140, T=512, ILP=2)
# test2(S=1, B=128, T=512, ILP=2)
# test(S=1, B=128, T=256, ILP=4)
# test(S=1, B=128, T=128, ILP=8)
# test(S=1, B=128, T=64, ILP=16)
@unittest.skipIf(not jt.compiler.has_cuda, "No CUDA found")
class TestBenchmarkCUDA(unittest.TestCase):
def setUp(self):
jt.flags.use_cuda = 1
def tearDown(self):
jt.flags.use_cuda = 0
def test_main(self):
return
get_mem_band()
check_simple_add_band()
if __name__ == "__main__":
unittest.main()

42
python/jittor/test/test_binary_op.py
View File

@ -19,12 +19,12 @@ def all_eq(x, y):
y = convert(y)
if str(x.dtype).startswith("float"):
return str(y.dtype).startswith("float") and x.shape == y.shape and (x==y).all()
return x.dtype == y.dtype and x.shape == y.shape and (x==y).all()
return x.dtype == y.dtype and x.shape == y.shape and np.testing.assert_allclose(x, y)
def check(op, *args):
x = eval(f"np.{op}(*args)")
y = eval(f"jt.{op}(*args).data")
assert all_eq(x, y), f"{x}\n{y}"
all_eq(x, y)
class TestBinaryOp(unittest.TestCase):
def test_binary_op(self):
@ -47,6 +47,9 @@ class TestBinaryOp(unittest.TestCase):
def test_i(self):
def check(op, a, b):
if isinstance(a, list):
a = np.array(a)
b = np.array(b)
if jt.flags.use_cuda and op == "@":
return
if op=="@":
@ -65,13 +68,13 @@ class TestBinaryOp(unittest.TestCase):
a = np.float32(a)
ja = np.float32(ja)
assert all_eq(ja, a), (ja,a)
all_eq(ja, a)
check("+", 5, 2)
check("-", 5, 2)
check("*", 5, 2)
check("/", 5, 2)
check("//", 5, 2)
check("@", [[5]], [[2]])
# check("@", [[5]], [[2]])
check("%", 5, 2)
check("**", 5, 2)
check("<<", 5, 2)
@ -80,6 +83,15 @@ class TestBinaryOp(unittest.TestCase):
check("^", 5, 2)
check("|", 5, 2)
check("+", [5.0,6.0], [2.0,3.0])
check("-", [5.0,6.0], [2.0,3.0])
check("*", [5.0,6.0], [2.0,3.0])
check("/", [5.0,6.0], [2.0,3.0])
check("//", [5.0,6.0], [2.0,3.0])
check("@", [[5,6],[7,8]], [[2,3],[4,5]])
check("%", [5.0,6.0], [2.0,3.0])
check("**", [5.0,6.0], [2.0,3.0])
def test_r(self):
def check(op, a, b):
a = np.array(a)
@ -97,7 +109,7 @@ class TestBinaryOp(unittest.TestCase):
a = eval(f"a {op} b")
a = np.array(a)
assert all_eq(jc, a), f"\n{jc}\n{a}"
all_eq(jc, a)
check("+", 5, 2)
check("-", 5, 2)
check("*", 5, 2)
@ -118,6 +130,7 @@ class TestBinaryOp(unittest.TestCase):
a = np.random.rand(10)
b = np.random.rand(10)
c = np.random.rand(10)
tol = 1e-2 if jt.flags.amp_reg & 2 else 1e-4
for op in ops:
func = lambda x: eval(f"((x[0]{op}x[1])*x[2]).sum()")
x, grads = ngrad(func, [a,b,c], 1e-8)
@ -127,7 +140,7 @@ class TestBinaryOp(unittest.TestCase):
jx = eval(f"(ja{op}jb)*jc")
jgrads = jt.grad(jx, [ja,jb,jc])
for jd, nd in zip(jgrads, grads):
assert (np.abs(jd.data-nd)<1e-4).all(), f"\n{jd.data}\n{nd}"
np.testing.assert_allclose(jd.data, nd, atol=tol, rtol=tol)
def test_mod_float(self):
a = jt.random((10,))
@ -137,7 +150,8 @@ class TestBinaryOp(unittest.TestCase):
a = jt.random((10,), 'float64')
b = jt.random((10,), 'float64')
c = a % b
assert np.allclose(c.data, a.data % b.data)
assert np.allclose(c.data, a.data % b.data, a.data, b.data)
if jt.flags.amp_reg & 2: return
a = jt.random((10,)) * 1000
b = (jt.random((10,)) * 10).int() + 1
c = a % b
@ -169,5 +183,19 @@ class TestBinaryOp(unittest.TestCase):
class TestBinaryOpCuda(TestBinaryOp, test_cuda(2)):
pass
class TestBinaryOpCpuFp16(TestBinaryOp):
def setUp(self):
jt.flags.amp_reg = 2 | 4 | 8 | 16
def tearDown(self):
jt.flags.amp_reg = 0
class TestBinaryOpCudaFp16(TestBinaryOp):
def setUp(self):
jt.flags.amp_reg = 2 | 4 | 8 | 16
jt.flags.use_cuda = 1
def tearDown(self):
jt.flags.amp_reg = 0
jt.flags.use_cuda = 0
if __name__ == "__main__":
unittest.main()

342
python/jittor/test/test_fp16.py Normal file
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@ -0,0 +1,342 @@
# ***************************************************************
# Copyright (c) 2021 Jittor. All Rights Reserved.
# Maintainers: Dun Liang <randonlang@gmail.com>.
# This file is subject to the terms and conditions defined in
# file 'LICENSE.txt', which is part of this source code package.
# ***************************************************************
import unittest
import jittor as jt
import numpy as np
import os
def transpose0231(x):
s0, s1, s2, s3 = x.shape
asize = 16
bsize = 16
ILP = 2
return jt.code([s0, s2, s3, s1], x.dtype, [x],
cuda_header="#include <type/fp16_compute.h>\n#include <cassert>",
cuda_src=f"""
__global__ void kernel(in0_type* __restrict__ x, in0_type* __restrict__ y, int s0, int s1, int s2, int s3) {{
__shared__ in0_type t[{asize*ILP}*{bsize*ILP+1}];
int t3 = threadIdx.x % {bsize};
int t1 = threadIdx.x / {bsize};
int b3 = blockIdx.x;
int b2 = blockIdx.y;
int b0 = blockIdx.z;
int x3 = 1;
int x2 = s3;
int x1 = s2*x2;
int x0 = s1*x1;
int y3 = 1;
int y2 = s1;
int y1 = s3*y2;
int y0 = s2*y1;
in0_type tmp[{ILP}];
for (int i=0; i<(s1-1)/{asize*ILP}+1; i++)
{{
int _b3 = b3 * {bsize*ILP} + t3*{ILP};
if (_b3 < s3) {{
#pragma unroll
for (int j=0; j<{ILP}; j++) {{
vload<sizeof(in0_type)*{ILP}>(
tmp,
&x[b0*x0+(t1*{ILP}+j+i*{asize*ILP})*x1+b2*x2+_b3*x3]
);
#pragma unroll
for (int k=0; k<{ILP}; k++)
t[(t1*{ILP}+j)*{bsize*ILP+1}+t3*{ILP}+k] = tmp[k];
}}
}}
__syncthreads();
int t3_ = threadIdx.x % {asize};
int t1_ = threadIdx.x / {asize};
_b3 = b3 * {bsize*ILP} + t1_*{ILP};
if (_b3 < s3) {{
#pragma unroll
for (int j=0; j<{ILP}; j++) {{
#pragma unroll
for (int k=0; k<{ILP}; k++) {{
tmp[k] =
t[(t3*{ILP}+k)*{bsize*ILP+1}+t1_*{ILP}+j];
}}
vload<sizeof(in0_type)*{ILP}>(
&y[b0*y0+b2*y1+(_b3+j)*y2+((t3*{ILP})+i*{asize*ILP})*y3],
tmp
);
}}
}}
__syncthreads();
}}
}}
int s0, s1, s2, s3;
in0->shape.unpack(s0, s1, s2, s3);
kernel<<<{{(s3-1)/{bsize*ILP}+1, s2, s0 }}, {bsize*asize}>>>
(in0_p, out0_p, s0, s1, s2, s3);
""")
def transpose0231_2(x):
s0, s1, s2, s3 = x.shape
asize = 16
bsize = 8
ILP = 2
return jt.code([s0, s2, s3, s1], x.dtype, [x],
cuda_header="#include <type/fp16_compute.h>\n#include <cassert>",
cuda_src=f"""
__global__ __launch_bounds__({asize*bsize}) void kernel(in0_type* __restrict__ x, in0_type* __restrict__ y, int s0, int s1, int s2, int s3) {{
__shared__ in0_type t[{asize*ILP}*{bsize*ILP+1}];
int t3 = threadIdx.x % {bsize};
int t1 = threadIdx.x / {bsize};
int b3 = blockIdx.x;
int b1 = blockIdx.y;
int b2 = 0;
int b0 = blockIdx.z;
int x3 = 1;
int x2 = s3;
int x1 = s2*x2;
int x0 = s1*x1;
int y3 = 1;
int y2 = s1;
int y1 = s3*y2;
int y0 = s2*y1;
in0_type tmp[{ILP}];
{{
int _b3 = b3 * {bsize*ILP} + t3*{ILP};
if (_b3 < s3) {{
#pragma unroll
for (int j=0; j<{ILP}; j++) {{
if (t1*{ILP}+j+b1*{asize*ILP} >= s1)
continue;
vload<sizeof(in0_type)*{ILP}>(
tmp,
&x[b0*x0+(t1*{ILP}+j+b1*{asize*ILP})*x1+b2*x2+_b3*x3]
);
#pragma unroll
for (int k=0; k<{ILP}; k++)
t[(t1*{ILP}+j)*{bsize*ILP+1}+t3*{ILP}+k] = tmp[k];
}}
}}
__syncthreads();
int t3_ = threadIdx.x % {asize};
int t1_ = threadIdx.x / {asize};
_b3 = b3 * {bsize*ILP} + t1_*{ILP};
int yy3 = (t3_*{ILP})+b1*{asize*ILP};
if (_b3 < s3 && yy3 < s1) {{
#pragma unroll
for (int j=0; j<{ILP}; j++) {{
#pragma unroll
for (int k=0; k<{ILP}; k++) {{
tmp[k] =
t[(t3_*{ILP}+k)*{bsize*ILP+1}+t1_*{ILP}+j];
}}
vload<sizeof(in0_type)*{ILP}>(
&y[b0*y0+b2*y1+(_b3+j)*y2+yy3*y3],
tmp
);
// printf("%d %d %d %d %d\\n", b0*y0+b2*y1+(_b3+j)*y2+yy3*y3,
// b0, b2, (_b3+j), yy3);
}}
}}
__syncthreads();
}}
}}
int s0, s1, s2, s3;
in0->shape.unpack(s0, s1, s2, s3);
kernel<<<{{(s3-1)/{bsize*ILP}+1, (s1-1)/{asize*ILP}+1, s0 }}, {bsize*asize}>>>
(in0_p, out0_p, s0, s1, s2, s3);
""")
def check_share():
return
a = jt.rand((30, 32, 4, 2000)).float32()
jt.code(a.shape, a.dtype, [a],
cuda_header="#include <type/fp16_compute.h>\n#include <cassert>",
cuda_src="""
__global__ void kernel(in0_type* __restrict__ a, in0_type* __restrict__ b) {
__shared__ float x[32*33];
for (int i=0; i<3; i++) {
((float2*)&x[i])[0] = ((float2*)&a[i])[0];
((float2*)&b[i])[0] = ((float2*)&x[i+1])[0];
}
}
kernel<<<1024,16*16>>>(in0_p, out0_p);
LOGir << "aaa";
""").sync()
jt.sync_all(True)
# print(a[0]+1)
print("pass test")
class TestFP16(unittest.TestCase):
def test_array(self):
a = np.array([1,2,3], dtype="float16")
b = jt.array(a)
np.testing.assert_allclose(a, b.data)
def test_add(self):
a = np.array([1,2,3], dtype="float16")
b = jt.array(a)
c = b+b
np.testing.assert_allclose(c.data, a+a)
d = c.sum()
np.testing.assert_allclose(d.data, [12])
c = c+1
print(c)
def test_matmul(self):
a = jt.random((100,100)).float16()
b = jt.random((100,100)).float16()
c = jt.matmul(a, b)
c.sync()
def test_matmul_grad(self):
a = jt.random((100,100)).float16()
b = jt.random((100,100)).float16()
c = jt.matmul(a, b)
c.sync()
da, db = jt.grad(c, [a,b])
jt.sync_all()
assert da.dtype == "float16"
assert db.dtype == "float16"
def test_array_random_auto_cast(self):
a = jt.array([1.0,2.0])
assert a.dtype == "float32"
with jt.flag_scope(amp_reg=2+16):
a = jt.array([1.0,2.0])
assert a.dtype == "float16", a.dtype
a = jt.random([10])
assert a.dtype == "float32"
with jt.flag_scope(amp_reg=2+16):
a = jt.random([10])
assert a.dtype == "float16", a.dtype
def test_conv(self):
a = jt.random((3,4,5,5)).float16()
b = jt.random((4,4,3,3)).float16()
c = jt.nn.conv(a, b)
c.sync()
def test_max(self):
a = jt.random((100,)).float16()
b = jt.random((100,)).float16()
c = a.maximum(b)
c.sync()
def test_reduce_dtype_infer(self):
with jt.flag_scope(amp_reg=1):
a = jt.random((3,4,5,5)).float16()
b = a.sum()
b.sync()
assert b.dtype == "float32"
with jt.flag_scope(amp_reg=2):
a = jt.random((3,4,5,5)).float16()
b = a.sum()
b.sync()
assert b.dtype == "float32"
with jt.flag_scope(amp_reg=0):
a = jt.random((3,4,5,5)).float16()
b = a.sum()
b.sync()
assert b.dtype == "float32"
with jt.flag_scope(amp_reg=2+4):
a = jt.random((3,4,5,5)).float16()
b = a.sum()
b.sync()
assert b.dtype == "float16", b.dtype
def test_white_dtype_infer(self):
with jt.flag_scope(amp_reg=1):
a = jt.random((3,4,5,5)).float16()
b = a**a
b.sync()
assert b.dtype == "float32"
with jt.flag_scope(amp_reg=2):
a = jt.random((3,4,5,5)).float16()
b = a**a
b.sync()
assert b.dtype == "float32"
with jt.flag_scope(amp_reg=0):
a = jt.random((3,4,5,5)).float16()
b = a**a
b.sync()
assert b.dtype == "float32"
with jt.flag_scope(amp_reg=2+8):
a = jt.random((3,4,5,5)).float16()
b = a**a
b.sync()
assert b.dtype == "float16", b.dtype
@unittest.skipIf(not jt.compiler.has_cuda, "No CUDA found")
class TestFP16CUDA(TestFP16):
def setUp(self):
jt.flags.use_cuda = 1
def tearDown(self):
jt.flags.use_cuda = 0
def test_softmax(self):
a = jt.rand((120, 2000, 2000)).float16()
# a = jt.rand((1, 2000, 2000)).float32()
jt.sync_all()
with jt.profile_scope(10, 100):
a.log_softmax(-1).sync()
def test_transpose(self):
check_share()
# return
a = jt.rand((30, 32, 4, 2000)).float32()
# a = jt.rand((1, 1024, 1, 2000)).float32()
diff = transpose0231(a).data != a.transpose((0,2,3,1)).data
print(np.where(diff))
# return
jt.sync_all()
# with jt.profile_scope(100, 11000):
with jt.profile_scope(100, 11000):
# a.log_softmax(-1).sync()
transpose0231(a).sync()
a.transpose((0,2,3,1)).sync()
# a.transpose((0,2,1,3)).sync()
a.fuse_transpose((0,2,1,3)).sync()
(a+1).sync()
jt.sync_all(True)
diff = transpose0231(a).data != a.transpose((0,2,3,1)).data
print(np.where(diff))
np.testing.assert_allclose(transpose0231(a).data, a.transpose((0,2,3,1)).data)
def test_transpose2(self):
# check_share()
# return
# a = jt.rand((30, 32, 4, 2000)).float32()
# a = jt.rand((1, 10000, 1, 2000)).float32()
a = jt.rand((1, 10000, 1, 2048)).float32()
print("transpose")
transpose0231_2(a).sync()
print("add")
(a+1).sync()
return
# a = jt.arange(32*16).reshape((1, 32, 1, 16))
diff = transpose0231_2(a).data != a.transpose((0,2,3,1)).data
print(np.where(diff))
# return
jt.sync_all()
# with jt.profile_scope(100, 11000):
with jt.profile_scope(100, 1100):
# a.log_softmax(-1).sync()
transpose0231_2(a).sync()
a.transpose((0,2,3,1)).sync()
# a.transpose((0,2,1,3)).sync()
a.fuse_transpose((0,2,1,3)).sync()
(a+1).sync()
jt.sync_all(True)
diff = transpose0231_2(a).data != a.transpose((0,2,3,1)).data
print(np.where(diff))
np.testing.assert_allclose(transpose0231_2(a).data, a.transpose((0,2,3,1)).data)
if __name__ == "__main__":
unittest.main()

31
python/jittor/test/test_misc_op.py
View File

@ -75,6 +75,8 @@ class TestPad(unittest.TestCase):
print('pass flip test ...')
def test_cross(self):
def check_equal(a, b, tol):
np.testing.assert_allclose(a.detach().numpy(), b.numpy(), atol=1e-5)
arr1 = np.random.randn(16,3,224,224,3)
arr2 = np.random.randn(16,3,224,224,3)
check_equal(torch.Tensor(arr1).cross(torch.Tensor(arr2), dim=1), jt.array(arr1).cross(jt.array(arr2), dim=1), 1e-1)
@ -257,34 +259,51 @@ class TestOther(unittest.TestCase):
a = jt.arctan2(jt.array([1,1.0,0]), jt.array([1,0.0,-1]))
np.testing.assert_allclose(a.data, [0.7853982,1.5707964,3.1415927])
y = jt.random((100,))
x = jt.random((100,))
z = jt.arctan2(y, x)
z2 = np.arctan2(y.data, x.data)
np.testing.assert_allclose(z.data, z2)
def test_code_softmax(self):
if not jt.has_cuda: return
def softmax(x, dim = None):
def softmax(x, dim = None, log=False):
if dim is None:
x = (x - x.max()).exp()
ret = x / x.sum()
else:
x = (x-x.max(dim, keepdims=True)).exp()
ret = x / x.sum(dim, keepdims=True)
if log: return ret.log()
return ret
from jittor.other.code_softmax import softmax_v1
with jt.flag_scope(use_cuda = 1):
shape = (120, 2000, 2000)
# shape = (3,3)
shape = (3,3)
for log in [0,1]:
for shape in [(3,3),
(12, 200, 2000),
(12, 200, 2048),
(12, 200, 2049)]:
print(shape)
a = jt.rand(shape)
c = jt.rand(shape)
b = softmax(a, -1)
bb = softmax_v1(a)
b = softmax(a, -1, log=log)
bb = softmax_v1(a, log=log)
err = (bb - b).abs().max()
assert err.item() < 1e-5
assert err.item() < 1e-5, (err, bb, b)
d1 = jt.grad(b*c, a)
d2 = jt.grad(bb*c, a)
err = (d1 - d2).abs().max()
assert err.item() < 1e-5
if log:
assert err.item() < 1e-2, (err.item())
else:
assert err.item() < 1e-5, (err.item())
if __name__ == "__main__":

39
python/jittor/test/test_resnet.py
View File

@ -36,19 +36,7 @@ class MnistNet(Module):
return x
@unittest.skipIf(skip_this_test, "skip_this_test")
class TestResnet(unittest.TestCase):
@classmethod
def setUpClass(self):
# hyper-parameters
self.batch_size = int(os.environ.get("TEST_BATCH_SIZE", "100"))
self.weight_decay = 0.0001
self.momentum = 0.9
self.learning_rate = 0.1
# mnist dataset
self.train_loader = MNIST(train=True, transform=trans.Resize(224)) \
.set_attrs(batch_size=self.batch_size, shuffle=True)
self.train_loader.num_workers = 4
class TestResnetFp32(unittest.TestCase):
# setup random seed
def setup_seed(self, seed):
np.random.seed(seed)
@ -59,6 +47,19 @@ class TestResnet(unittest.TestCase):
@jt.flag_scope(use_cuda=1, use_stat_allocator=1)
def test_resnet(self):
self.setup_seed(1)
# hyper-parameters
self.batch_size = int(os.environ.get("TEST_BATCH_SIZE", "100"))
self.weight_decay = 0.0001
self.momentum = 0.9
self.learning_rate = 0.1
if jt.flags.amp_reg:
self.learning_rate = 0.01
# mnist dataset
self.train_loader = MNIST(train=True, transform=trans.Resize(224)) \
.set_attrs(batch_size=self.batch_size, shuffle=True)
self.train_loader.num_workers = 4
loss_list=[]
acc_list=[]
mnist_net = MnistNet()
@ -70,6 +71,7 @@ class TestResnet(unittest.TestCase):
for data, target in self.train_loader:
batch_id = self.train_loader.batch_id
epoch_id = self.train_loader.epoch_id
data = data.float_auto()
# train step
# with jt.log_capture_scope(
@ -120,6 +122,8 @@ class TestResnet(unittest.TestCase):
# Train Epoch: 0 [40/100 (40%)] Loss: 2.286762 Acc: 0.130000
# Train Epoch: 0 [50/100 (50%)] Loss: 2.055014 Acc: 0.290000
if jt.flags.amp_reg:
continue
if jt.in_mpi:
assert jt.core.number_of_lived_vars() < 8100, jt.core.number_of_lived_vars()
else:
@ -131,5 +135,14 @@ class TestResnet(unittest.TestCase):
assert np.mean(loss_list[-50:])<0.5
assert np.mean(acc_list[-50:])>0.8
@unittest.skipIf(skip_this_test, "skip_this_test")
class TestResnetFp16(TestResnetFp32):
def setup(self):
jt.flags.auto_mixed_precision_level = 5
def tearDown(self):
jt.flags.auto_mixed_precision_level = 0
if __name__ == "__main__":
unittest.main()

121
python/jittor/test/test_superglue.py Normal file
View File

@ -0,0 +1,121 @@
# ***************************************************************
# Copyright (c) 2021 Jittor. All Rights Reserved.
# Maintainers: Dun Liang <randonlang@gmail.com>.
# This file is subject to the terms and conditions defined in
# file 'LICENSE.txt', which is part of this source code package.
# ***************************************************************
import unittest
import jittor as jt
import numpy as np
import os
from jittor.test.misc import superglue
from jittor.test.misc.superglue import SuperGlue
import time
@jt.flag_scope(use_cuda=1)
def main():
global superglue
superglue.split_size = int(os.environ.get("split_size", "12"))
# superglue.split_size = 1000000
batch = 30
num = 2000
dim = 128
# jt.display_memory_info()
# os.system("nvidia-smi")
# breakpoint()
with jt.no_grad():
config = {
'superglue': {
'sinkhorn_iterations': 25,
'match_threshold': 0.01,
'keypoint_position_dim': 2,
'descriptor_dim': dim,
'use_dual_softmax': True,
'GNN_layers': ['self', 'cross'] * 9,
}
}
superglue = SuperGlue(config.get('superglue', {}))
superglue.eval()
data = {
'keypoints0': jt.rand((batch, num, 2), dtype=jt.float),
'keypoints1': jt.rand((batch, num, 2), dtype=jt.float),
'shape0': jt.rand((batch, 2), dtype=jt.float),
'shape1': jt.rand((batch, 2), dtype=jt.float),
'descriptors0': jt.rand((batch, dim, num), dtype=jt.float),
'descriptors1': jt.rand((batch, dim, num), dtype=jt.float),
'scores0': jt.rand((batch, num), dtype=jt.float),
'scores1': jt.rand((batch, num), dtype=jt.float),
'all_matches': jt.randint(0, num, (batch, num, 2), dtype=jt.int),
'return_match': False,
# 'match_num': match_num
}
use_fp16 = int(os.environ.get("use_fp16", "0"))
if use_fp16:
jt.flags.amp_reg = 2
for k,v in data.items():
if isinstance(v, jt.Var) and v.dtype == "float32":
v.assign(v.float16())
for v in superglue.parameters():
if v.dtype == "float32":
v.assign(v.float16())
jt.sync_all(True)
import pickle
jt.sync_all(True)
for x in range(5):
print(x)
jt.gc()
x = superglue(data)['loss']
x.sync()
jt.display_memory_info()
# os.system("nvidia-smi")
# breakpoint()
# print(data)
# print(x)
# with open("/tmp/record.pkl", "wb") as f:
# pickle.dump([data, x], f, pickle.HIGHEST_PROTOCOL)
# with jt.flag_scope(trace_py_var=3, profile_memory_enable=1):
# x = superglue(data)['loss']
# x.sync()
# jt.get_max_memory_treemap()
# exit(0)
jt.sync_all(True)
time0 = time.time()
jt.flags.profiler_enable = int(os.environ.get("profiler", "0"))
for x in range(20):
print(x)
# jt.display_memory_info()
x = superglue(data)['loss']
x.sync()
# print(x)
jt.sync_all(True)
time1 = time.time()
print("avg time:", (time1 - time0) / 20)
return (time1 - time0) / 20
class TestSuperglue(unittest.TestCase):
def test(self):
if not jt.has_cuda: return
t1 = main()
os.environ["use_fp16"] = "1"
t2 = main()
os.environ["use_fp16"] = "0"
assert t1*0.55 > t2
if __name__ == "__main__":
unittest.main()

30
python/jittor/test/test_unary_op.py
View File

@ -17,7 +17,8 @@ def check(op, *args):
x = convert(x)
y = convert(y)
# str match nan and inf
assert x.dtype == y.dtype and x.shape == y.shape
assert x.dtype == y.dtype and x.shape == y.shape, \
(x.dtype, y.dtype, x.shape, y.shape)
for a,b in zip(x.flatten(), y.flatten()):
assert str(a)[:5] == str(b)[:5], (a,b)
@ -32,9 +33,10 @@ class TestUnaryOp(unittest.TestCase):
check("logical_not", a)
check("bitwise_not", a)
b = np.array([1.1, 2.2, 3.3, 4.4, -1, 0])
check("log", a.astype("float32"))
check("exp", a.astype("float32"))
check("sqrt", a.astype("float32"))
type = "float16" if (jt.flags.amp_reg & 2) else "float32"
check("log", a.astype(type))
check("exp", a.astype(type))
check("sqrt", a.astype(type))
def test_grad(self):
ops = ["abs", "negative", "log", "exp", "sqrt",
@ -60,7 +62,8 @@ class TestUnaryOp(unittest.TestCase):
ja = jt.array(b)
jb = eval(f"jt.{op}(ja)")
jda = jt.grad(jb, ja)
assert (np.allclose(jda.data, da)), (jda.data,da,op)
tol = 1e-2 if jt.flags.amp_reg & 2 else 1e-6
assert (np.allclose(jda.data, da, atol=tol, rtol=tol)), (jda.data,da,op)
def test_sigmoid(self):
a = np.arange(-150,150, 10).astype("float32")
@ -92,11 +95,26 @@ class TestUnaryOp(unittest.TestCase):
np.testing.assert_allclose(y.data, y2.data)
d = jt.grad(x2, y2)
_, (dn,) = ngrad(lambda y: special.erfinv(y).sum(), [y], 1e-8)
np.testing.assert_allclose(d.data, dn, atol=1e-6, rtol=1e-6)
tol = 1e-3 if jt.flags.amp_reg & 2 else 1e-6
np.testing.assert_allclose(d.data, dn, atol=tol, rtol=tol)
class TestUnaryOpCuda(TestUnaryOp, test_cuda(2)):
pass
class TestUnaryOpCudaFp16(TestUnaryOp, test_cuda(2)):
def setUp(self):
jt.flags.amp_reg = 2 | 4 | 8 | 16
def tearDown(self):
jt.flags.amp_reg = 0
class TestUnaryOpCudaFp16(TestUnaryOp, test_cuda(2)):
def setUp(self):
jt.flags.amp_reg = 2 | 4 | 8 | 16
jt.flags.use_cuda = 1
def tearDown(self):
jt.flags.amp_reg = 0
jt.flags.use_cuda = 0
if __name__ == "__main__":
unittest.main()

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