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kernels-bot commited on Aug 25

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build/torch212-cxx11-xpu20253-x86_64-linux/__init__.py +75 -0
build/torch212-cxx11-xpu20253-x86_64-linux/_activation_xpu_48c70b2.abi3.so +3 -0
build/torch212-cxx11-xpu20253-x86_64-linux/_ops.py +9 -0
build/torch212-cxx11-xpu20253-x86_64-linux/layers.py +201 -0
build/torch212-cxx11-xpu20253-x86_64-linux/metadata.json +31 -0
build/torch212-cxx11-xpu20253-x86_64-linux/metadata.json.sigstore +1 -0
build/torch213-cxx11-xpu20260-x86_64-linux/__init__.py +75 -0
build/torch213-cxx11-xpu20260-x86_64-linux/_activation_xpu_48c70b2.abi3.so +3 -0
build/torch213-cxx11-xpu20260-x86_64-linux/_ops.py +9 -0
build/torch213-cxx11-xpu20260-x86_64-linux/layers.py +201 -0
build/torch213-cxx11-xpu20260-x86_64-linux/metadata.json +31 -0
build/torch213-cxx11-xpu20260-x86_64-linux/metadata.json.sigstore +1 -0

build/torch212-cxx11-xpu20253-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,75 @@

+import torch
+from ._ops import ops
+from . import layers
+def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.silu_and_mul(out, x)
+    return out
+def mul_and_silu(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.mul_and_silu(out, x)
+    return out
+def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu_and_mul(out, x)
+    return out
+def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu_tanh_and_mul(out, x)
+    return out
+def fatrelu_and_mul(out: torch.Tensor, x: torch.Tensor, threshold: float = 0.0) -> torch.Tensor:
+    ops.fatrelu_and_mul(out, x, threshold)
+    return out
+def gelu(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu(out, x)
+    return out
+def silu(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.silu(out, x)
+    return out
+def gelu_tanh(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu_tanh(out, x)
+    return out
+def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu_fast(out, x)
+    return out
+def gelu_new(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu_new(out, x)
+    return out
+def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu_quick(out, x)
+    return out
+__all__ = [
+    "silu_and_mul",
+    "mul_and_silu",
+    "gelu_and_mul",
+    "gelu_tanh_and_mul",
+    "fatrelu_and_mul",
+    "gelu_fast",
+    "gelu_new",
+    "gelu_quick",
+    "gelu_tanh",
+    "silu",
+    "gelu",
+    "layers",
+]

build/torch212-cxx11-xpu20253-x86_64-linux/_activation_xpu_48c70b2.abi3.so ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:f6cd10f5f0554fe63f5665d013101cf0ece9f52c7aecf19882245fd3ea924436
+size 947784

build/torch212-cxx11-xpu20253-x86_64-linux/_ops.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import torch
+from . import _activation_xpu_48c70b2
+ops = torch.ops._activation_xpu_48c70b2
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_activation_xpu_48c70b2::{op_name}"

build/torch212-cxx11-xpu20253-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,201 @@

+import torch
+import torch.nn as nn
+from ._ops import ops
+class SiluAndMul(nn.Module):
+    """An activation function for SwiGLU.
+    The function computes x -> silu(x[:d]) * x[d:] where d = x.shape[-1] // 2.
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.silu_and_mul(out, x)
+        return out
+class Silu(nn.Module):
+    """An activation function for SiLU.
+    The function computes x -> silu(x).
+    Shapes:
+        x: (num_tokens, d) or (batch_size, seq_len, d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        out = torch.empty_like(x)
+        ops.silu(out, x)
+        return out
+class Gelu(nn.Module):
+    """An activation function for GELU.
+    The function computes x -> gelu(x).
+    Shapes:
+        x: (num_tokens, d) or (batch_size, seq_len, d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        out = torch.empty_like(x)
+        ops.gelu(out, x)
+        return out
+class GeluTanh(nn.Module):
+    """An activation function for GELU with `tanh` approximation.
+    The function computes x -> gelu_tanh(x).
+    Shapes:
+        x: (num_tokens, d) or (batch_size, seq_len, d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        out = torch.empty_like(x)
+        ops.gelu_tanh(out, x)
+        return out
+class MulAndSilu(nn.Module):
+    """An activation function for SwiGLU.
+    The function computes x -> x[:d] * silu(x[d:]) where d = x.shape[-1] // 2.
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if not x.is_contiguous():
+            x = x.contiguous()
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.mul_and_silu(out, x)
+        return out
+class GeluAndMul(nn.Module):
+    """An activation function for GeGLU.
+    The function computes x -> GELU(x[:d]) * x[d:] where d = x.shape[-1] // 2.
+    Shapes:
+        x: (batch_size, seq_len, 2 * d) or (num_tokens, 2 * d)
+        return: (batch_size, seq_len, d) or (num_tokens, d)
+    """
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.gelu_and_mul(out, x)
+        return out
+class GeluTanhAndMul(nn.Module):
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.gelu_tanh_and_mul(out, x)
+        return out
+class FatreluAndMul(nn.Module):
+    """An activation function for FATReLU.
+    The function computes x -> FATReLU(x[:d]) * x[d:] where
+    d = x.shape[-1] // 2.
+    This is used in openbmb/MiniCPM-S-1B-sft.
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+    can_torch_compile: bool = True
+    def __init__(self, threshold: float = 0.0):
+        super().__init__()
+        self.threshold = threshold
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.fatrelu_and_mul(out, x, self.threshold)
+        return out
+class FastGELU(nn.Module):
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if not x.is_contiguous():
+            x = x.contiguous()
+        out = torch.empty_like(x)
+        ops.gelu_fast(out, x)
+        return out
+class NewGELU(nn.Module):
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if not x.is_contiguous():
+            x = x.contiguous()
+        out = torch.empty_like(x)
+        ops.gelu_new(out, x)
+        return out
+class QuickGELU(nn.Module):
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if not x.is_contiguous():
+            x = x.contiguous()
+        out = torch.empty_like(x)
+        ops.gelu_quick(out, x)
+        return out

build/torch212-cxx11-xpu20253-x86_64-linux/metadata.json ADDED Viewed

	@@ -0,0 +1,31 @@

+{
+  "name": "activation",
+  "id": "_activation_xpu_48c70b2",
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "xpu"
+  },
+  "digest": {
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+    "files": {
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+      "_activation_xpu_48c70b2.abi3.so": "9s0Q9fBVT+Y/VmXQExAc8Ozp9Sx67PGYgiRf0+qSRDY=",
+      "_ops.py": "bnTZQDC/nPRug4m7O+3urq7Wog8gHfHGpSGak6zrx0o=",
+      "layers.py": "wWX07+W0BNFA7RyCE0jYH7aa2U3B8aiiIpu/Q4xsE0w="
+    }
+  },
+  "provenance": {
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+      "version": "0.17.0-dev0",
+      "commit": "18c4d686a271ca6e56d0110fd36a9a5f58e86215",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "48c70b2ba7aef180abf5842ee848a2fbf6ac7938",
+      "dirty": false
+    }
+  }
+}

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build/torch213-cxx11-xpu20260-x86_64-linux/__init__.py ADDED Viewed

	@@ -0,0 +1,75 @@

+import torch
+from ._ops import ops
+from . import layers
+def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.silu_and_mul(out, x)
+    return out
+def mul_and_silu(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.mul_and_silu(out, x)
+    return out
+def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu_and_mul(out, x)
+    return out
+def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu_tanh_and_mul(out, x)
+    return out
+def fatrelu_and_mul(out: torch.Tensor, x: torch.Tensor, threshold: float = 0.0) -> torch.Tensor:
+    ops.fatrelu_and_mul(out, x, threshold)
+    return out
+def gelu(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu(out, x)
+    return out
+def silu(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.silu(out, x)
+    return out
+def gelu_tanh(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu_tanh(out, x)
+    return out
+def gelu_fast(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu_fast(out, x)
+    return out
+def gelu_new(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu_new(out, x)
+    return out
+def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    ops.gelu_quick(out, x)
+    return out
+__all__ = [
+    "silu_and_mul",
+    "mul_and_silu",
+    "gelu_and_mul",
+    "gelu_tanh_and_mul",
+    "fatrelu_and_mul",
+    "gelu_fast",
+    "gelu_new",
+    "gelu_quick",
+    "gelu_tanh",
+    "silu",
+    "gelu",
+    "layers",
+]

build/torch213-cxx11-xpu20260-x86_64-linux/_activation_xpu_48c70b2.abi3.so ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:3ea5dec8a81959e2125267c71b804d485a397119c7fff05fbe03b955978bbd71
+size 893328

build/torch213-cxx11-xpu20260-x86_64-linux/_ops.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import torch
+from . import _activation_xpu_48c70b2
+ops = torch.ops._activation_xpu_48c70b2
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_activation_xpu_48c70b2::{op_name}"

build/torch213-cxx11-xpu20260-x86_64-linux/layers.py ADDED Viewed

	@@ -0,0 +1,201 @@

+import torch
+import torch.nn as nn
+from ._ops import ops
+class SiluAndMul(nn.Module):
+    """An activation function for SwiGLU.
+    The function computes x -> silu(x[:d]) * x[d:] where d = x.shape[-1] // 2.
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.silu_and_mul(out, x)
+        return out
+class Silu(nn.Module):
+    """An activation function for SiLU.
+    The function computes x -> silu(x).
+    Shapes:
+        x: (num_tokens, d) or (batch_size, seq_len, d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        out = torch.empty_like(x)
+        ops.silu(out, x)
+        return out
+class Gelu(nn.Module):
+    """An activation function for GELU.
+    The function computes x -> gelu(x).
+    Shapes:
+        x: (num_tokens, d) or (batch_size, seq_len, d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        out = torch.empty_like(x)
+        ops.gelu(out, x)
+        return out
+class GeluTanh(nn.Module):
+    """An activation function for GELU with `tanh` approximation.
+    The function computes x -> gelu_tanh(x).
+    Shapes:
+        x: (num_tokens, d) or (batch_size, seq_len, d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        out = torch.empty_like(x)
+        ops.gelu_tanh(out, x)
+        return out
+class MulAndSilu(nn.Module):
+    """An activation function for SwiGLU.
+    The function computes x -> x[:d] * silu(x[d:]) where d = x.shape[-1] // 2.
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if not x.is_contiguous():
+            x = x.contiguous()
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.mul_and_silu(out, x)
+        return out
+class GeluAndMul(nn.Module):
+    """An activation function for GeGLU.
+    The function computes x -> GELU(x[:d]) * x[d:] where d = x.shape[-1] // 2.
+    Shapes:
+        x: (batch_size, seq_len, 2 * d) or (num_tokens, 2 * d)
+        return: (batch_size, seq_len, d) or (num_tokens, d)
+    """
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.gelu_and_mul(out, x)
+        return out
+class GeluTanhAndMul(nn.Module):
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.gelu_tanh_and_mul(out, x)
+        return out
+class FatreluAndMul(nn.Module):
+    """An activation function for FATReLU.
+    The function computes x -> FATReLU(x[:d]) * x[d:] where
+    d = x.shape[-1] // 2.
+    This is used in openbmb/MiniCPM-S-1B-sft.
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+    can_torch_compile: bool = True
+    def __init__(self, threshold: float = 0.0):
+        super().__init__()
+        self.threshold = threshold
+    def forward(self, x: torch.Tensor):
+        if not x.is_contiguous():
+            x = x.contiguous()
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        ops.fatrelu_and_mul(out, x, self.threshold)
+        return out
+class FastGELU(nn.Module):
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if not x.is_contiguous():
+            x = x.contiguous()
+        out = torch.empty_like(x)
+        ops.gelu_fast(out, x)
+        return out
+class NewGELU(nn.Module):
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if not x.is_contiguous():
+            x = x.contiguous()
+        out = torch.empty_like(x)
+        ops.gelu_new(out, x)
+        return out
+class QuickGELU(nn.Module):
+    can_torch_compile: bool = True
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if not x.is_contiguous():
+            x = x.contiguous()
+        out = torch.empty_like(x)
+        ops.gelu_quick(out, x)
+        return out

build/torch213-cxx11-xpu20260-x86_64-linux/metadata.json ADDED Viewed

	@@ -0,0 +1,31 @@

+{
+  "name": "activation",
+  "id": "_activation_xpu_48c70b2",
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "xpu"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "NQdhY7p9BPoMw0NQodMKFTPSQXBlrsX3yUdP8MEHyH0=",
+      "_activation_xpu_48c70b2.abi3.so": "PqXeyKgZWeISUmfHG4BNSFo5cRnH//BfvgO5VZeLvXE=",
+      "_ops.py": "bnTZQDC/nPRug4m7O+3urq7Wog8gHfHGpSGak6zrx0o=",
+      "layers.py": "wWX07+W0BNFA7RyCE0jYH7aa2U3B8aiiIpu/Q4xsE0w="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.17.0-dev0",
+      "commit": "18c4d686a271ca6e56d0110fd36a9a5f58e86215",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "48c70b2ba7aef180abf5842ee848a2fbf6ac7938",
+      "dirty": false
+    }
+  }
+}

build/torch213-cxx11-xpu20260-x86_64-linux/metadata.json.sigstore ADDED Viewed

	@@ -0,0 +1 @@

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