Merge pull request #569 from pengcheng888/issue/565

issue/565 - 调整一些的算子到infinicore.nn.functional.py文件中

.gitignore

@@ -9,6 +9,9 @@ python/infinicore/lib/*.so
 # Vscode
 .vscode/
 
+# Pycharm
+.idea/
+
 # Python
 __pycache__/
 *.egg-info/

python/infinicore/__init__.py

@@ -27,13 +27,10 @@ from infinicore.dtype import (
 from infinicore.ntops import use_ntops
 from infinicore.ops.add import add
 from infinicore.ops.attention import attention
-from infinicore.ops.causal_softmax import causal_softmax
 from infinicore.ops.matmul import matmul
 from infinicore.ops.rearrange import rearrange
-from infinicore.ops.rms_norm import rms_norm
-from infinicore.ops.silu import silu
-from infinicore.ops.swiglu import swiglu
 from infinicore.tensor import (
+    Tensor,
     empty,
     from_blob,
     ones,
@@ -42,6 +39,8 @@ from infinicore.tensor import (
     zeros,
 )
 
+from infinicore import nn as nn
+
 __all__ = [
     # Classes.
     "device",
@@ -74,12 +73,8 @@ __all__ = [
     # Operations.
     "add",
     "attention",
-    "causal_softmax",
     "matmul",
     "rearrange",
-    "rms_norm",
-    "silu",
-    "swiglu",
     "empty",
     "from_blob",
     "ones",

python/infinicore/nn/__init__.py

+from infinicore.nn import (
+    functional as functional,
+)

python/infinicore/nn/functional.py

+import infinicore
+from infinicore.lib import _infinicore
+
+__all__ = ["causal_softmax", "rms_norm", "silu", "swiglu"]
+
+
+def causal_softmax(
+        input: infinicore.Tensor,
+        out=None
+) -> infinicore.Tensor:
+    r"""Apply a causal softmax function.
+    """
+
+    if out is None:
+        return infinicore.Tensor(_infinicore.causal_softmax(input._underlying))
+
+    _infinicore.causal_softmax_(out._underlying, input._underlying)
+
+    return out
+
+
+def rms_norm(
+        input: infinicore.Tensor,
+        normalized_shape: list[int],
+        weight: infinicore.Tensor,
+        eps: float = 1e-5,
+        out=None
+) -> infinicore.Tensor:
+    r"""Apply Root Mean Square Layer Normalization.
+    """
+
+    assert normalized_shape == weight.shape, "normalized_shape does not match weight.shape."
+
+    if out is None:
+        return infinicore.Tensor(
+            _infinicore.rms_norm(input._underlying, weight._underlying, eps)
+        )
+
+    _infinicore.rms_norm_(out._underlying, input._underlying, weight._underlying, eps)
+
+    return out
+
+
+def silu(input: infinicore.Tensor, inplace: bool = False, out=None) -> infinicore.Tensor:
+    r"""Apply the Sigmoid Linear Unit (SiLU) function, element-wise.
+    """
+
+    if inplace:
+        _infinicore.silu_(input._underlying, input._underlying)
+        return input
+
+    if out is None:
+        return infinicore.Tensor(_infinicore.silu(input._underlying))
+
+    _infinicore.silu_(out._underlying, input._underlying)
+
+    return out
+
+
+def swiglu(input: infinicore.Tensor, other: infinicore.Tensor, out=None):
+    r"""Apply the Swish-Gated Linear Unit (SwiGLU) function, element-wise.
+    """
+
+    if out is None:
+        return infinicore.Tensor(_infinicore.swiglu(input._underlying, other._underlying))
+
+    _infinicore.swiglu_(out._underlying, input._underlying, other._underlying)
+
+    return out

python/infinicore/ops/causal_softmax.py

-from infinicore.lib import _infinicore
-from infinicore.tensor import Tensor
-
-
-def causal_softmax(input, *, out=None):
-    if out is None:
-        return Tensor(_infinicore.causal_softmax(input._underlying))
-
-    _infinicore.causal_softmax_(out._underlying, input._underlying)
-
-    return out

python/infinicore/ops/rms_norm.py

-from infinicore.lib import _infinicore
-from infinicore.tensor import Tensor
-
-
-def rms_norm(input, weight, epsilon=1e-5, *, out=None):
-    if out is None:
-        return Tensor(
-            _infinicore.rms_norm(input._underlying, weight._underlying, epsilon)
-        )
-
-    _infinicore.rms_norm_(
-        out._underlying, input._underlying, weight._underlying, epsilon
-    )
-
-    return out

python/infinicore/ops/silu.py

-from infinicore.lib import _infinicore
-from infinicore.tensor import Tensor
-
-
-def silu(input, *, out=None):
-    if out is None:
-        return Tensor(_infinicore.silu(input._underlying))
-
-    _infinicore.silu_(out._underlying, input._underlying)
-
-    return out

python/infinicore/ops/swiglu.py

-from infinicore.lib import _infinicore
-from infinicore.tensor import Tensor
-
-
-def swiglu(input, other, *, out=None):
-    if out is None:
-        return Tensor(_infinicore.swiglu(input._underlying, other._underlying))
-
-    _infinicore.swiglu_(out._underlying, input._underlying, other._underlying)
-
-    return out

test/infinicore/ops/causal_softmax.py

@@ -132,7 +132,9 @@ class OpTest(BaseOperatorTest):
         return self.torch_causal_softmax(*args, **kwargs)
 
     def infinicore_operator(self, *args, **kwargs):
-        return infinicore.causal_softmax(*args, **kwargs)
+        import infinicore.nn.functional as F
+
+        return F.causal_softmax(*args, **kwargs)
 
 
 def main():

test/infinicore/ops/rms_norm.py

@@ -149,7 +149,9 @@ class OpTest(BaseOperatorTest):
 
     def infinicore_operator(self, x, weight, epsilon=_EPSILON, out=None, **kwargs):
         """InfiniCore RMSNorm implementation"""
-        return infinicore.rms_norm(x, weight, epsilon, out=out)
+        import infinicore.nn.functional as F
+        
+        return F.rms_norm(x, weight.shape, weight, epsilon, out=out)
 
 
 def main():

test/infinicore/ops/silu.py

@@ -130,7 +130,9 @@ class OpTest(BaseOperatorTest):
 
     def infinicore_operator(self, input, out=None, **kwargs):
         """InfiniCore SiLU implementation"""
-        return infinicore.silu(input, out=out)
+        import infinicore.nn.functional as F
+        
+        return F.silu(input, out=out)
 
 
 def main():

test/infinicore/ops/swiglu.py

@@ -146,7 +146,9 @@ class OpTest(BaseOperatorTest):
 
     def infinicore_operator(self, a, b, out=None, **kwargs):
         """InfiniCore SwiGLU implementation"""
-        return infinicore.swiglu(a, b, out=out)
+        import infinicore.nn.functional as F
+        
+        return F.swiglu(a, b, out=out)
 
 
 def main():