Fix PReLU Broadcasting Bug for Multiple Parameters

README.md

@@ -382,10 +382,10 @@ Email: [email protected]
 
 File an issue: https://github.com/Jittor/jittor/issues
 
-QQ Group: 761222083
+QQ Group: 836860279
 
 
-<img src="https://cg.cs.tsinghua.edu.cn/jittor/images/news/2020-12-8-21-19-1_2_2/fig4.png" width="200"/>
+<img src="https://github.com/Jittor/jittor/assets/62846124/8dd830bd-b31c-4e4f-9a78-5fd7a3409145" width="200"/>
 
 ## The Team
 

python/jittor/__init__.py

@@ -9,7 +9,7 @@
 # file 'LICENSE.txt', which is part of this source code package.
 # ***************************************************************
 
-__version__ = '1.3.9.6'
+__version__ = '1.3.9.10'
 from jittor_utils import lock
 with lock.lock_scope():
     ori_int = int
@@ -428,7 +428,9 @@ def random(shape, dtype="float32", type="uniform"):
         jt.Var([[0.96788853 0.28334728 0.30482838]
                 [0.46107793 0.62798643 0.03457401]], dtype=float32)
     '''
-
+    for dim in shape:
+        if dim < 0:
+            raise RuntimeError(f"Trying to create tensor with negative dimension {dim}: {shape}")
     ret = ops.random(shape, "float32", type)
    ## TODO: move those code to core
    #if dtype in ["float16", "bfloat16"]:
@@ -484,6 +486,9 @@ def ones(*shape, dtype="float32"):
         shape = shape[:-1]
     if isinstance(shape, tuple) and isinstance(shape[0], (Sequence, NanoVector)):
         shape = shape[0]
+    for dim in shape:
+        if dim < 0:
+            raise RuntimeError(f"Trying to create tensor with negative dimension {dim}: {shape}")
     return unary(1, dtype).broadcast(shape)
 
 def new_ones(x, size):
@@ -515,6 +520,9 @@ def zeros(*shape, dtype="float32"):
         shape = shape[:-1]
     if isinstance(shape, tuple) and isinstance(shape[0], (Sequence, NanoVector)):
         shape = shape[0]
+    for dim in shape:
+        if dim < 0:
+            raise RuntimeError(f"Trying to create tensor with negative dimension {dim}: {shape}")
     return unary(0, dtype).broadcast(shape)
 
 def new_zeros(x, size):
@@ -547,6 +555,9 @@ def full(shape,val,dtype="float32"):
     '''
     if not isinstance(shape, (NanoVector, Sequence)):
         shape = (shape,)
+    for dim in shape:
+        if dim < 0:
+            raise RuntimeError(f"Trying to create tensor with negative dimension {dim}: {shape}")
     return unary(val, dtype).broadcast(shape)
 
 def new_full(x, size, val):
@@ -687,6 +698,8 @@ def flatten(input, start_dim=0, end_dim=-1):
     start_dim = len(in_shape) + start_dim if start_dim < 0 else start_dim
     end_dim = len(in_shape) + end_dim if end_dim < 0 else end_dim
     assert end_dim >= start_dim, "end_dim should be larger than or equal to start_dim for flatten function"
+    if len(in_shape) <= end_dim:
+        raise IndexError(f"Dimension out of range (expected to be in range of [{-len(in_shape)}, {len(in_shape) - 1}], but got {end_dim})")
     out_shape = []
     for i in range(0,start_dim,1): out_shape.append(in_shape[i])
     dims = 1
@@ -917,6 +930,9 @@ def randn(*size, dtype="float32", requires_grad=True) -> Var:
          [-0.612632   -1.1471151  -1.1879086 ]], dtype=float32)
     '''
     if isinstance(size, tuple) and isinstance(size[0], (tuple, list, NanoVector)): size = size[0]
+    for dim in size:
+        if dim < 0:
+            raise RuntimeError(f"Trying to create tensor with negative dimension {dim}: {size}")
     arr = jt.random(size, dtype, "normal")
     if not requires_grad: return arr.stop_grad()
     return arr
@@ -1013,6 +1029,9 @@ def randint(low, high=None, shape=(1,), dtype="int32") -> Var:
                 [1 1 1]], dtype=int32)
     '''
     if high is None: low, high = 0, low
+    for dim in shape:
+        if dim < 0:
+            raise RuntimeError(f"Trying to create tensor with negative dimension {dim}: {shape}")
     v = (jt.random(shape) * (high - low) + low).clamp(low, high-0.5)
     v = jt.floor_int(v)
     return v.astype(dtype)
@@ -2152,3 +2171,7 @@ def inplace_wrapper(new_k, prev_func):
 from . import math_util
 from .math_util import *
 from . import distributions
+
+if jt.compiler.has_acl:
+    from jittor.extern.acl.acl_compiler import change_function
+    change_function()