We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
output accumulation value is very negative
After training for a long time ~81,990 steps on instant ngp, get the following stack trace:

![Screenshot from 2022-07-12 10-56-19](https://user-images.githubusercontent.com/14854811/178564226-68c72f1d-1ebf-4fa6-ba0e-9942a161aed0.png)


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `pyrad/fields/instant_ngp_field.py`
Content:
```
1 # Copyright 2022 The Plenoptix Team. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 
15 """
16 Instant-NGP field implementations using tiny-cuda-nn, torch, ....
17 """
18 
19 
20 from typing import Tuple
21 
22 import torch
23 from torch.nn.parameter import Parameter
24 
25 from pyrad.fields.modules.encoding import Encoding, HashEncoding, SHEncoding
26 from pyrad.fields.modules.field_heads import FieldHeadNames
27 from pyrad.fields.base import Field
28 from pyrad.fields.nerf_field import NeRFField
29 from pyrad.cameras.rays import RaySamples
30 from pyrad.utils.activations import trunc_exp
31 
32 try:
33     import tinycudann as tcnn
34 except ImportError:
35     # tinycudann module doesn't exist
36     pass
37 
38 
39 def get_normalized_positions(positions, aabb):
40     """Return normalized positions in range [0, 1] based on the aabb axis-aligned bounding box."""
41     aabb_lengths = aabb[1] - aabb[0]
42     positions = (positions - aabb[0]) / aabb_lengths
43     return positions
44 
45 
46 def get_normalized_directions(directions):
47     """SH encoding must be in the range [0, 1]"""
48     return (directions + 1.0) / 2.0
49 
50 
51 class TCNNInstantNGPField(Field):
52     """NeRF Field"""
53 
54     def __init__(
55         self, aabb, num_layers=2, hidden_dim=64, geo_feat_dim=15, num_layers_color=3, hidden_dim_color=64
56     ) -> None:
57         super().__init__()
58 
59         self.aabb = Parameter(aabb, requires_grad=False)
60 
61         self.geo_feat_dim = geo_feat_dim
62 
63         # TODO: set this properly based on the aabb
64         per_level_scale = 1.4472692012786865
65 
66         self.position_encoding = tcnn.Encoding(
67             n_input_dims=3,
68             encoding_config={
69                 "otype": "HashGrid",
70                 "n_levels": 16,
71                 "n_features_per_level": 2,
72                 "log2_hashmap_size": 19,
73                 "base_resolution": 16,
74                 "per_level_scale": per_level_scale,
75             },
76         )
77 
78         self.direction_encoding = tcnn.Encoding(
79             n_input_dims=3,
80             encoding_config={
81                 "otype": "SphericalHarmonics",
82                 "degree": 4,
83             },
84         )
85 
86         self.mlp_base = tcnn.Network(
87             n_input_dims=32,
88             n_output_dims=1 + self.geo_feat_dim,
89             network_config={
90                 "otype": "FullyFusedMLP",
91                 "activation": "ReLU",
92                 "output_activation": "None",
93                 "n_neurons": hidden_dim,
94                 "n_hidden_layers": num_layers - 1,
95             },
96         )
97 
98         self.mlp_head = tcnn.Network(
99             n_input_dims=self.direction_encoding.n_output_dims + self.geo_feat_dim,
100             n_output_dims=3,
101             network_config={
102                 "otype": "FullyFusedMLP",
103                 "activation": "ReLU",
104                 "output_activation": "None",
105                 "n_neurons": hidden_dim_color,
106                 "n_hidden_layers": num_layers_color - 1,
107             },
108         )
109 
110     def get_density(self, ray_samples: RaySamples):
111         """Computes and returns the densities."""
112         positions = get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)
113         positions_flat = positions.view(-1, 3)
114         dtype = positions_flat.dtype
115         x = self.position_encoding(positions_flat)
116         h = self.mlp_base(x).view(*ray_samples.frustums.get_positions().shape[:-1], -1).to(dtype)
117         density_before_activation, base_mlp_out = torch.split(h, [1, self.geo_feat_dim], dim=-1)
118 
119         # Rectifying the density with an exponential is much more stable than a ReLU or
120         # softplus, because it enables high post-activation (float32) density outputs
121         # from smaller internal (float16) parameters.
122         assert density_before_activation.dtype is torch.float32
123         density = trunc_exp(density_before_activation)
124         return density, base_mlp_out
125 
126     def get_outputs(self, ray_samples: RaySamples, density_embedding=None):
127         # TODO: add valid_mask masking!
128         # tcnn requires directions in the range [0,1]
129         directions = get_normalized_directions(ray_samples.frustums.directions)
130         directions_flat = directions.view(-1, 3)
131         dtype = directions_flat.dtype
132         d = self.direction_encoding(directions_flat)
133         h = torch.cat([d, density_embedding.view(-1, self.geo_feat_dim)], dim=-1)
134         h = self.mlp_head(h).view(*ray_samples.frustums.directions.shape[:-1], -1).to(dtype)
135         rgb = torch.sigmoid(h)
136         return {FieldHeadNames.RGB: rgb}
137 
138 
139 class TorchInstantNGPField(NeRFField):
140     """
141     PyTorch implementation of the instant-ngp field.
142     """
143 
144     def __init__(
145         self,
146         aabb,
147         position_encoding: Encoding = HashEncoding(),
148         direction_encoding: Encoding = SHEncoding(),
149         base_mlp_num_layers: int = 3,
150         base_mlp_layer_width: int = 64,
151         head_mlp_num_layers: int = 2,
152         head_mlp_layer_width: int = 32,
153         skip_connections: Tuple = (4,),
154     ) -> None:
155         super().__init__(
156             position_encoding,
157             direction_encoding,
158             base_mlp_num_layers,
159             base_mlp_layer_width,
160             head_mlp_num_layers,
161             head_mlp_layer_width,
162             skip_connections,
163         )
164         self.aabb = Parameter(aabb, requires_grad=False)
165 
166     def get_density(self, ray_samples: RaySamples):
167         normalized_ray_samples = ray_samples
168         normalized_ray_samples.positions = get_normalized_positions(
169             normalized_ray_samples.frustums.get_positions(), self.aabb
170         )
171         return super().get_density(normalized_ray_samples)
172 
173 
174 field_implementation_to_class = {"tcnn": TCNNInstantNGPField, "torch": TorchInstantNGPField}
175
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/pyrad/fields/instant_ngp_field.py b/pyrad/fields/instant_ngp_field.py
--- a/pyrad/fields/instant_ngp_field.py
+++ b/pyrad/fields/instant_ngp_field.py
@@ -101,7 +101,7 @@
             network_config={
                 "otype": "FullyFusedMLP",
                 "activation": "ReLU",
-                "output_activation": "None",
+                "output_activation": "Sigmoid",
                 "n_neurons": hidden_dim_color,
                 "n_hidden_layers": num_layers_color - 1,
             },
@@ -131,8 +131,8 @@
         dtype = directions_flat.dtype
         d = self.direction_encoding(directions_flat)
         h = torch.cat([d, density_embedding.view(-1, self.geo_feat_dim)], dim=-1)
-        h = self.mlp_head(h).view(*ray_samples.frustums.directions.shape[:-1], -1).to(dtype)
-        rgb = torch.sigmoid(h)
+        rgb = self.mlp_head(h).view(*ray_samples.frustums.directions.shape[:-1], -1).to(dtype)
+        assert rgb.dtype is torch.float32
         return {FieldHeadNames.RGB: rgb}

{"golden_diff": "diff --git a/pyrad/fields/instant_ngp_field.py b/pyrad/fields/instant_ngp_field.py\n--- a/pyrad/fields/instant_ngp_field.py\n+++ b/pyrad/fields/instant_ngp_field.py\n@@ -101,7 +101,7 @@\n             network_config={\n                 \"otype\": \"FullyFusedMLP\",\n                 \"activation\": \"ReLU\",\n-                \"output_activation\": \"None\",\n+                \"output_activation\": \"Sigmoid\",\n                 \"n_neurons\": hidden_dim_color,\n                 \"n_hidden_layers\": num_layers_color - 1,\n             },\n@@ -131,8 +131,8 @@\n         dtype = directions_flat.dtype\n         d = self.direction_encoding(directions_flat)\n         h = torch.cat([d, density_embedding.view(-1, self.geo_feat_dim)], dim=-1)\n-        h = self.mlp_head(h).view(*ray_samples.frustums.directions.shape[:-1], -1).to(dtype)\n-        rgb = torch.sigmoid(h)\n+        rgb = self.mlp_head(h).view(*ray_samples.frustums.directions.shape[:-1], -1).to(dtype)\n+        assert rgb.dtype is torch.float32\n         return {FieldHeadNames.RGB: rgb}\n", "issue": "output accumulation value is very negative\nAfter training for a long time ~81,990 steps on instant ngp, get the following stack trace:\r\n\r\n![Screenshot from 2022-07-12 10-56-19](https://user-images.githubusercontent.com/14854811/178564226-68c72f1d-1ebf-4fa6-ba0e-9942a161aed0.png)\r\n\n", "before_files": [{"content": "# Copyright 2022 The Plenoptix Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nInstant-NGP field implementations using tiny-cuda-nn, torch, ....\n\"\"\"\n\n\nfrom typing import Tuple\n\nimport torch\nfrom torch.nn.parameter import Parameter\n\nfrom pyrad.fields.modules.encoding import Encoding, HashEncoding, SHEncoding\nfrom pyrad.fields.modules.field_heads import FieldHeadNames\nfrom pyrad.fields.base import Field\nfrom pyrad.fields.nerf_field import NeRFField\nfrom pyrad.cameras.rays import RaySamples\nfrom pyrad.utils.activations import trunc_exp\n\ntry:\n    import tinycudann as tcnn\nexcept ImportError:\n    # tinycudann module doesn't exist\n    pass\n\n\ndef get_normalized_positions(positions, aabb):\n    \"\"\"Return normalized positions in range [0, 1] based on the aabb axis-aligned bounding box.\"\"\"\n    aabb_lengths = aabb[1] - aabb[0]\n    positions = (positions - aabb[0]) / aabb_lengths\n    return positions\n\n\ndef get_normalized_directions(directions):\n    \"\"\"SH encoding must be in the range [0, 1]\"\"\"\n    return (directions + 1.0) / 2.0\n\n\nclass TCNNInstantNGPField(Field):\n    \"\"\"NeRF Field\"\"\"\n\n    def __init__(\n        self, aabb, num_layers=2, hidden_dim=64, geo_feat_dim=15, num_layers_color=3, hidden_dim_color=64\n    ) -> None:\n        super().__init__()\n\n        self.aabb = Parameter(aabb, requires_grad=False)\n\n        self.geo_feat_dim = geo_feat_dim\n\n        # TODO: set this properly based on the aabb\n        per_level_scale = 1.4472692012786865\n\n        self.position_encoding = tcnn.Encoding(\n            n_input_dims=3,\n            encoding_config={\n                \"otype\": \"HashGrid\",\n                \"n_levels\": 16,\n                \"n_features_per_level\": 2,\n                \"log2_hashmap_size\": 19,\n                \"base_resolution\": 16,\n                \"per_level_scale\": per_level_scale,\n            },\n        )\n\n        self.direction_encoding = tcnn.Encoding(\n            n_input_dims=3,\n            encoding_config={\n                \"otype\": \"SphericalHarmonics\",\n                \"degree\": 4,\n            },\n        )\n\n        self.mlp_base = tcnn.Network(\n            n_input_dims=32,\n            n_output_dims=1 + self.geo_feat_dim,\n            network_config={\n                \"otype\": \"FullyFusedMLP\",\n                \"activation\": \"ReLU\",\n                \"output_activation\": \"None\",\n                \"n_neurons\": hidden_dim,\n                \"n_hidden_layers\": num_layers - 1,\n            },\n        )\n\n        self.mlp_head = tcnn.Network(\n            n_input_dims=self.direction_encoding.n_output_dims + self.geo_feat_dim,\n            n_output_dims=3,\n            network_config={\n                \"otype\": \"FullyFusedMLP\",\n                \"activation\": \"ReLU\",\n                \"output_activation\": \"None\",\n                \"n_neurons\": hidden_dim_color,\n                \"n_hidden_layers\": num_layers_color - 1,\n            },\n        )\n\n    def get_density(self, ray_samples: RaySamples):\n        \"\"\"Computes and returns the densities.\"\"\"\n        positions = get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)\n        positions_flat = positions.view(-1, 3)\n        dtype = positions_flat.dtype\n        x = self.position_encoding(positions_flat)\n        h = self.mlp_base(x).view(*ray_samples.frustums.get_positions().shape[:-1], -1).to(dtype)\n        density_before_activation, base_mlp_out = torch.split(h, [1, self.geo_feat_dim], dim=-1)\n\n        # Rectifying the density with an exponential is much more stable than a ReLU or\n        # softplus, because it enables high post-activation (float32) density outputs\n        # from smaller internal (float16) parameters.\n        assert density_before_activation.dtype is torch.float32\n        density = trunc_exp(density_before_activation)\n        return density, base_mlp_out\n\n    def get_outputs(self, ray_samples: RaySamples, density_embedding=None):\n        # TODO: add valid_mask masking!\n        # tcnn requires directions in the range [0,1]\n        directions = get_normalized_directions(ray_samples.frustums.directions)\n        directions_flat = directions.view(-1, 3)\n        dtype = directions_flat.dtype\n        d = self.direction_encoding(directions_flat)\n        h = torch.cat([d, density_embedding.view(-1, self.geo_feat_dim)], dim=-1)\n        h = self.mlp_head(h).view(*ray_samples.frustums.directions.shape[:-1], -1).to(dtype)\n        rgb = torch.sigmoid(h)\n        return {FieldHeadNames.RGB: rgb}\n\n\nclass TorchInstantNGPField(NeRFField):\n    \"\"\"\n    PyTorch implementation of the instant-ngp field.\n    \"\"\"\n\n    def __init__(\n        self,\n        aabb,\n        position_encoding: Encoding = HashEncoding(),\n        direction_encoding: Encoding = SHEncoding(),\n        base_mlp_num_layers: int = 3,\n        base_mlp_layer_width: int = 64,\n        head_mlp_num_layers: int = 2,\n        head_mlp_layer_width: int = 32,\n        skip_connections: Tuple = (4,),\n    ) -> None:\n        super().__init__(\n            position_encoding,\n            direction_encoding,\n            base_mlp_num_layers,\n            base_mlp_layer_width,\n            head_mlp_num_layers,\n            head_mlp_layer_width,\n            skip_connections,\n        )\n        self.aabb = Parameter(aabb, requires_grad=False)\n\n    def get_density(self, ray_samples: RaySamples):\n        normalized_ray_samples = ray_samples\n        normalized_ray_samples.positions = get_normalized_positions(\n            normalized_ray_samples.frustums.get_positions(), self.aabb\n        )\n        return super().get_density(normalized_ray_samples)\n\n\nfield_implementation_to_class = {\"tcnn\": TCNNInstantNGPField, \"torch\": TorchInstantNGPField}\n", "path": "pyrad/fields/instant_ngp_field.py"}], "after_files": [{"content": "# Copyright 2022 The Plenoptix Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nInstant-NGP field implementations using tiny-cuda-nn, torch, ....\n\"\"\"\n\n\nfrom typing import Tuple\n\nimport torch\nfrom torch.nn.parameter import Parameter\n\nfrom pyrad.fields.modules.encoding import Encoding, HashEncoding, SHEncoding\nfrom pyrad.fields.modules.field_heads import FieldHeadNames\nfrom pyrad.fields.base import Field\nfrom pyrad.fields.nerf_field import NeRFField\nfrom pyrad.cameras.rays import RaySamples\nfrom pyrad.utils.activations import trunc_exp\n\ntry:\n    import tinycudann as tcnn\nexcept ImportError:\n    # tinycudann module doesn't exist\n    pass\n\n\ndef get_normalized_positions(positions, aabb):\n    \"\"\"Return normalized positions in range [0, 1] based on the aabb axis-aligned bounding box.\"\"\"\n    aabb_lengths = aabb[1] - aabb[0]\n    positions = (positions - aabb[0]) / aabb_lengths\n    return positions\n\n\ndef get_normalized_directions(directions):\n    \"\"\"SH encoding must be in the range [0, 1]\"\"\"\n    return (directions + 1.0) / 2.0\n\n\nclass TCNNInstantNGPField(Field):\n    \"\"\"NeRF Field\"\"\"\n\n    def __init__(\n        self, aabb, num_layers=2, hidden_dim=64, geo_feat_dim=15, num_layers_color=3, hidden_dim_color=64\n    ) -> None:\n        super().__init__()\n\n        self.aabb = Parameter(aabb, requires_grad=False)\n\n        self.geo_feat_dim = geo_feat_dim\n\n        # TODO: set this properly based on the aabb\n        per_level_scale = 1.4472692012786865\n\n        self.position_encoding = tcnn.Encoding(\n            n_input_dims=3,\n            encoding_config={\n                \"otype\": \"HashGrid\",\n                \"n_levels\": 16,\n                \"n_features_per_level\": 2,\n                \"log2_hashmap_size\": 19,\n                \"base_resolution\": 16,\n                \"per_level_scale\": per_level_scale,\n            },\n        )\n\n        self.direction_encoding = tcnn.Encoding(\n            n_input_dims=3,\n            encoding_config={\n                \"otype\": \"SphericalHarmonics\",\n                \"degree\": 4,\n            },\n        )\n\n        self.mlp_base = tcnn.Network(\n            n_input_dims=32,\n            n_output_dims=1 + self.geo_feat_dim,\n            network_config={\n                \"otype\": \"FullyFusedMLP\",\n                \"activation\": \"ReLU\",\n                \"output_activation\": \"None\",\n                \"n_neurons\": hidden_dim,\n                \"n_hidden_layers\": num_layers - 1,\n            },\n        )\n\n        self.mlp_head = tcnn.Network(\n            n_input_dims=self.direction_encoding.n_output_dims + self.geo_feat_dim,\n            n_output_dims=3,\n            network_config={\n                \"otype\": \"FullyFusedMLP\",\n                \"activation\": \"ReLU\",\n                \"output_activation\": \"Sigmoid\",\n                \"n_neurons\": hidden_dim_color,\n                \"n_hidden_layers\": num_layers_color - 1,\n            },\n        )\n\n    def get_density(self, ray_samples: RaySamples):\n        \"\"\"Computes and returns the densities.\"\"\"\n        positions = get_normalized_positions(ray_samples.frustums.get_positions(), self.aabb)\n        positions_flat = positions.view(-1, 3)\n        dtype = positions_flat.dtype\n        x = self.position_encoding(positions_flat)\n        h = self.mlp_base(x).view(*ray_samples.frustums.get_positions().shape[:-1], -1).to(dtype)\n        density_before_activation, base_mlp_out = torch.split(h, [1, self.geo_feat_dim], dim=-1)\n\n        # Rectifying the density with an exponential is much more stable than a ReLU or\n        # softplus, because it enables high post-activation (float32) density outputs\n        # from smaller internal (float16) parameters.\n        assert density_before_activation.dtype is torch.float32\n        density = trunc_exp(density_before_activation)\n        return density, base_mlp_out\n\n    def get_outputs(self, ray_samples: RaySamples, density_embedding=None):\n        # TODO: add valid_mask masking!\n        # tcnn requires directions in the range [0,1]\n        directions = get_normalized_directions(ray_samples.frustums.directions)\n        directions_flat = directions.view(-1, 3)\n        dtype = directions_flat.dtype\n        d = self.direction_encoding(directions_flat)\n        h = torch.cat([d, density_embedding.view(-1, self.geo_feat_dim)], dim=-1)\n        rgb = self.mlp_head(h).view(*ray_samples.frustums.directions.shape[:-1], -1).to(dtype)\n        assert rgb.dtype is torch.float32\n        return {FieldHeadNames.RGB: rgb}\n\n\nclass TorchInstantNGPField(NeRFField):\n    \"\"\"\n    PyTorch implementation of the instant-ngp field.\n    \"\"\"\n\n    def __init__(\n        self,\n        aabb,\n        position_encoding: Encoding = HashEncoding(),\n        direction_encoding: Encoding = SHEncoding(),\n        base_mlp_num_layers: int = 3,\n        base_mlp_layer_width: int = 64,\n        head_mlp_num_layers: int = 2,\n        head_mlp_layer_width: int = 32,\n        skip_connections: Tuple = (4,),\n    ) -> None:\n        super().__init__(\n            position_encoding,\n            direction_encoding,\n            base_mlp_num_layers,\n            base_mlp_layer_width,\n            head_mlp_num_layers,\n            head_mlp_layer_width,\n            skip_connections,\n        )\n        self.aabb = Parameter(aabb, requires_grad=False)\n\n    def get_density(self, ray_samples: RaySamples):\n        normalized_ray_samples = ray_samples\n        normalized_ray_samples.positions = get_normalized_positions(\n            normalized_ray_samples.frustums.get_positions(), self.aabb\n        )\n        return super().get_density(normalized_ray_samples)\n\n\nfield_implementation_to_class = {\"tcnn\": TCNNInstantNGPField, \"torch\": TorchInstantNGPField}\n", "path": "pyrad/fields/instant_ngp_field.py"}]}