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kernels-bot commited on Jul 9

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build/torch-stable-abi210-cu126-x86_64-linux/__init__.py +75 -0
build/torch-stable-abi210-cu126-x86_64-linux/_activation_cuda_2f7c452.abi3.so +3 -0
build/torch-stable-abi210-cu126-x86_64-linux/_ops.py +9 -0
build/torch-stable-abi210-cu126-x86_64-linux/activation/__init__.py +26 -0
build/torch-stable-abi210-cu126-x86_64-linux/layers.py +201 -0
build/torch-stable-abi210-cu126-x86_64-linux/metadata.json +41 -0
build/torch-stable-abi210-cu126-x86_64-linux/metadata.json.sigstore +1 -0
build/torch-stable-abi210-cu128-x86_64-linux/__init__.py +75 -0
build/torch-stable-abi210-cu128-x86_64-linux/_activation_cuda_2f7c452.abi3.so +3 -0
build/torch-stable-abi210-cu128-x86_64-linux/_ops.py +9 -0
build/torch-stable-abi210-cu128-x86_64-linux/activation/__init__.py +26 -0
build/torch-stable-abi210-cu128-x86_64-linux/layers.py +201 -0
build/torch-stable-abi210-cu128-x86_64-linux/metadata.json +44 -0
build/torch-stable-abi210-cu128-x86_64-linux/metadata.json.sigstore +1 -0
build/torch-stable-abi210-cu130-x86_64-linux/__init__.py +75 -0
build/torch-stable-abi210-cu130-x86_64-linux/_activation_cuda_2f7c452.abi3.so +3 -0
build/torch-stable-abi210-cu130-x86_64-linux/_ops.py +9 -0
build/torch-stable-abi210-cu130-x86_64-linux/activation/__init__.py +26 -0
build/torch-stable-abi210-cu130-x86_64-linux/layers.py +201 -0
build/torch-stable-abi210-cu130-x86_64-linux/metadata.json +43 -0
build/torch-stable-abi210-cu130-x86_64-linux/metadata.json.sigstore +1 -0
build/torch-stable-abi210-cu132-x86_64-linux/__init__.py +75 -0
build/torch-stable-abi210-cu132-x86_64-linux/_activation_cuda_2f7c452.abi3.so +3 -0
build/torch-stable-abi210-cu132-x86_64-linux/_ops.py +9 -0
build/torch-stable-abi210-cu132-x86_64-linux/activation/__init__.py +26 -0
build/torch-stable-abi210-cu132-x86_64-linux/layers.py +201 -0
build/torch-stable-abi210-cu132-x86_64-linux/metadata.json +43 -0
build/torch-stable-abi210-cu132-x86_64-linux/metadata.json.sigstore +1 -0

build/torch-stable-abi210-cu126-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/torch-stable-abi210-cu126-x86_64-linux/_activation_cuda_2f7c452.abi3.so ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:b73105f386072409d3ca2efb024422fd7e31ed086e4f7c308c2166dd44b6b5fb
+size 1214312

build/torch-stable-abi210-cu126-x86_64-linux/_ops.py ADDED Viewed

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

+import torch
+from . import _activation_cuda_2f7c452
+ops = torch.ops._activation_cuda_2f7c452
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_activation_cuda_2f7c452::{op_name}"

build/torch-stable-abi210-cu126-x86_64-linux/activation/__init__.py ADDED Viewed

	@@ -0,0 +1,26 @@

+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch-stable-abi210-cu126-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/torch-stable-abi210-cu126-x86_64-linux/metadata.json ADDED Viewed

	@@ -0,0 +1,41 @@

+{
+  "name": "activation",
+  "id": "_activation_cuda_2f7c452",
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "7.0",
+      "7.2",
+      "7.5",
+      "8.0",
+      "8.6",
+      "8.7",
+      "8.9",
+      "9.0+PTX"
+    ]
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "NQdhY7p9BPoMw0NQodMKFTPSQXBlrsX3yUdP8MEHyH0=",
+      "_activation_cuda_2f7c452.abi3.so": "tzEF84YHJAnTyi77AkQi/X4x7QhuT3wwjCFm3US2tfs=",
+      "_ops.py": "q9xZtwPjdsRciOCA5aQFDtsTQnV9SNcvAC74ivbShuM=",
+      "activation/__init__.py": "DFYPlrhXwYjEqCl/8n0SmWGZV8NFml5DPhMjKfv98GY=",
+      "layers.py": "wWX07+W0BNFA7RyCE0jYH7aa2U3B8aiiIpu/Q4xsE0w="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.17.0-dev0",
+      "sha": "81580bb92577f2f7228661ca2a221fc052375709",
+      "dirty": false
+    },
+    "kernel": {
+      "sha": "2f7c452801641012cf6aa9bdf042431457024033",
+      "dirty": false
+    }
+  }
+}

build/torch-stable-abi210-cu126-x86_64-linux/metadata.json.sigstore ADDED Viewed

	@@ -0,0 +1 @@

+ 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build/torch-stable-abi210-cu128-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/torch-stable-abi210-cu128-x86_64-linux/_activation_cuda_2f7c452.abi3.so ADDED Viewed

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+version https://git-lfs.github.com/spec/v1
+oid sha256:06e673344bb8a9fdcbc772403ad784e1e652371b41adadc9df230118c2bda83d
+size 2439096

build/torch-stable-abi210-cu128-x86_64-linux/_ops.py ADDED Viewed

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

+import torch
+from . import _activation_cuda_2f7c452
+ops = torch.ops._activation_cuda_2f7c452
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_activation_cuda_2f7c452::{op_name}"

build/torch-stable-abi210-cu128-x86_64-linux/activation/__init__.py ADDED Viewed

	@@ -0,0 +1,26 @@

+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch-stable-abi210-cu128-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/torch-stable-abi210-cu128-x86_64-linux/metadata.json ADDED Viewed

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+  "name": "activation",
+  "id": "_activation_cuda_2f7c452",
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "10.1",
+      "12.0+PTX",
+      "7.0",
+      "7.2",
+      "7.5",
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+      "8.7",
+      "8.9",
+      "9.0"
+    ]
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
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+      "_activation_cuda_2f7c452.abi3.so": "BuZzNEu4qf3Lx3JAOteE4eZSNxtBra3J3yMBGMK9qD0=",
+      "_ops.py": "q9xZtwPjdsRciOCA5aQFDtsTQnV9SNcvAC74ivbShuM=",
+      "activation/__init__.py": "DFYPlrhXwYjEqCl/8n0SmWGZV8NFml5DPhMjKfv98GY=",
+      "layers.py": "wWX07+W0BNFA7RyCE0jYH7aa2U3B8aiiIpu/Q4xsE0w="
+    }
+  },
+  "provenance": {
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+    "kernel": {
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+      "dirty": false
+    }
+  }
+}

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build/torch-stable-abi210-cu130-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/torch-stable-abi210-cu130-x86_64-linux/_activation_cuda_2f7c452.abi3.so ADDED Viewed

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4a3850b428f3b640028c451c8e25cb66b9ec57a90878d34fcabd1248541e1952
+size 2615936

build/torch-stable-abi210-cu130-x86_64-linux/_ops.py ADDED Viewed

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

+import torch
+from . import _activation_cuda_2f7c452
+ops = torch.ops._activation_cuda_2f7c452
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_activation_cuda_2f7c452::{op_name}"

build/torch-stable-abi210-cu130-x86_64-linux/activation/__init__.py ADDED Viewed

	@@ -0,0 +1,26 @@

+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch-stable-abi210-cu130-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/torch-stable-abi210-cu130-x86_64-linux/metadata.json ADDED Viewed

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+  "name": "activation",
+  "id": "_activation_cuda_2f7c452",
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
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+      "12.0",
+      "12.1+PTX",
+      "7.5",
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+      "8.7",
+      "8.9",
+      "9.0"
+    ]
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
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+      "_ops.py": "q9xZtwPjdsRciOCA5aQFDtsTQnV9SNcvAC74ivbShuM=",
+      "activation/__init__.py": "DFYPlrhXwYjEqCl/8n0SmWGZV8NFml5DPhMjKfv98GY=",
+      "layers.py": "wWX07+W0BNFA7RyCE0jYH7aa2U3B8aiiIpu/Q4xsE0w="
+    }
+  },
+  "provenance": {
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+    "kernel": {
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+      "dirty": false
+    }
+  }
+}

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build/torch-stable-abi210-cu132-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/torch-stable-abi210-cu132-x86_64-linux/_activation_cuda_2f7c452.abi3.so ADDED Viewed

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+version https://git-lfs.github.com/spec/v1
+oid sha256:72f4044f3bcf933e96214ab600657dab57acaca2243081c713b218541e59be22
+size 2687952

build/torch-stable-abi210-cu132-x86_64-linux/_ops.py ADDED Viewed

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

+import torch
+from . import _activation_cuda_2f7c452
+ops = torch.ops._activation_cuda_2f7c452
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_activation_cuda_2f7c452::{op_name}"

build/torch-stable-abi210-cu132-x86_64-linux/activation/__init__.py ADDED Viewed

	@@ -0,0 +1,26 @@

+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch-stable-abi210-cu132-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/torch-stable-abi210-cu132-x86_64-linux/metadata.json ADDED Viewed

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+  "name": "activation",
+  "id": "_activation_cuda_2f7c452",
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
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+      "11.0",
+      "12.0",
+      "12.1+PTX",
+      "7.5",
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+      "8.7",
+      "8.9",
+      "9.0"
+    ]
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
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+      "_ops.py": "q9xZtwPjdsRciOCA5aQFDtsTQnV9SNcvAC74ivbShuM=",
+      "activation/__init__.py": "DFYPlrhXwYjEqCl/8n0SmWGZV8NFml5DPhMjKfv98GY=",
+      "layers.py": "wWX07+W0BNFA7RyCE0jYH7aa2U3B8aiiIpu/Q4xsE0w="
+    }
+  },
+  "provenance": {
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+      "dirty": false
+    }
+  }
+}

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