chore: import upstream snapshot with attribution

2026-07-13 13:36:55 +08:00
commit c8a779b1bb
1887 changed files with 3245738 additions and 0 deletions
@@ -0,0 +1,102 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from polygraphy import mod
+from polygraphy.logger import G_LOGGER
+
+np = mod.lazy_import("numpy")
+gs = mod.lazy_import("onnx_graphsurgeon")
+
+OP_REGISTRY = {}  # Dict[str, Callable]: Maps op names to reference implementations
+
+
+def register(op):
+    """
+    Registers a function as the reference implementation for a given op.
+
+    Args:
+         op (str): The name of the op for which to register this function.
+    """
+
+    def register_impl(func):
+        def wrapped_func(node, intermediate_tensors):
+            inputs = []
+            for inp in node.inputs:
+                if inp.is_empty():  # Optional input
+                    inputs.append(None)
+                elif isinstance(inp, gs.Constant):
+                    inputs.append(inp.values)
+                elif inp.name in intermediate_tensors:
+                    inputs.append(intermediate_tensors[inp.name])
+                else:
+                    G_LOGGER.internal_error(
+                        f"Input: {inp.name} was not found in intermediate tensors and is not a constant.\nNote: Intermediate tensors include: {list(intermediate_tensors.keys())}"
+                    )
+
+            outputs = func(node.attrs, *inputs)
+            if len(outputs) != len(node.outputs):
+                G_LOGGER.internal_error(
+                    f"{op} reference implementation returned the wrong number of outputs.\nNote: Expected {len(node.outputs)} but recevied {len(outputs)}"
+                )
+
+            return {
+                out_tensor.name: out for out_tensor, out in zip(node.outputs, outputs)
+            }
+
+        OP_REGISTRY[op] = wrapped_func
+        return wrapped_func
+
+    return register_impl
+
+
+@register("Identity")
+def run_identity(attrs, x):
+    return [x]
+
+
+@register("InstanceNormalization")
+def run_instancenorm(attrs, x, weights, bias):
+    epsilon = attrs.get("epsilon", 1.0e-5)
+
+    rank = len(x.shape)
+    axis = tuple(range(2, rank))
+    mean = np.mean(x, axis=axis, keepdims=True)
+    var = np.var(x, axis=axis, keepdims=True)
+
+    # Weights and bias needs to be broadcasted to shape of X. C dimension should be a wildcard.
+    broadcast_shape = [-1] + [1] * (rank - 2)
+    weights = weights.reshape(broadcast_shape)
+    bias = bias.reshape(broadcast_shape)
+
+    res = weights * (x - mean) / np.sqrt(var + epsilon) + bias
+    return [res]
+
+
+@register("MeanVarianceNormalization")
+def run_meanvarnorm(attrs, x):
+    epsilon = 1.0e-9
+    axes = attrs.get("axes", [0, 2, 3])
+    axes = tuple(axes)
+
+    data_mean = np.mean(x, axis=axes, keepdims=True)
+    data_mean_squared = np.power(data_mean, 2)
+    data_squared = np.power(x, 2)
+    data_squared_mean = np.mean(data_squared, axis=axes, keepdims=True)
+    std = np.sqrt(data_squared_mean - data_mean_squared)
+    res = (x - data_mean) / (std + epsilon)
+
+    return [res]