From 960c7dea74740880d312921c01b12e0c511b53d5 Mon Sep 17 00:00:00 2001 From: David Gardner Date: Tue, 26 Mar 2024 16:44:33 -0700 Subject: [PATCH 1/7] Creating branch for v24.06 From 3f62724bcc81cc57b778cd391c969d7656cd4dca Mon Sep 17 00:00:00 2001 From: David Gardner Date: Tue, 26 Mar 2024 16:46:19 -0700 Subject: [PATCH 2/7] Updating versions for v24.06.00 --- anomalous-auth-detection/README.md | 2 +- dga-detection/README.md | 6 +++--- gnn-asset-clustering/README.md | 2 +- ids-detection/README.md | 2 +- log-sequence-ad/README.md | 2 +- 5 files changed, 7 insertions(+), 7 deletions(-) diff --git a/anomalous-auth-detection/README.md b/anomalous-auth-detection/README.md index 6fb7605..50e72c7 100644 --- a/anomalous-auth-detection/README.md +++ b/anomalous-auth-detection/README.md @@ -179,7 +179,7 @@ or "fraudulent" authentication.
### What training is recommended for developers working with this model? If none, please state "none." * None ### Link the relevant end user license agreement -* [Apache 2.0](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/LICENSE) +* [Apache 2.0](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/LICENSE) ## Model Card ++ Saftey & Security Subcard diff --git a/dga-detection/README.md b/dga-detection/README.md index 7b381e6..819b60b 100644 --- a/dga-detection/README.md +++ b/dga-detection/README.md @@ -95,7 +95,7 @@ limitations under the License. **Link:**
-* https://github.com/nv-morpheus/morpheus-experimental/blob/branch-24.03/dga-detection/datasets/dga-training-data.csv
+* https://github.com/nv-morpheus/morpheus-experimental/blob/branch-24.06/dga-detection/datasets/dga-training-data.csv
**Properties (Quantity, Dataset Descriptions, Sensor(s)):**
@@ -109,7 +109,7 @@ limitations under the License. **Link:**
-* https://github.com/nv-morpheus/morpheus-experimental/blob/branch-24.03/dga-detection/datasets/dga-validation-data.csv
+* https://github.com/nv-morpheus/morpheus-experimental/blob/branch-24.06/dga-detection/datasets/dga-validation-data.csv
**Properties (Quantity, Dataset Descriptions, Sensor(s)):** @@ -232,7 +232,7 @@ limitations under the License. ### Link the location of the training dataset's repository. -* https://github.com/nv-morpheus/morpheus-experimental/blob/branch-24.03/dga-detection/datasets/dga-training-data.csv +* https://github.com/nv-morpheus/morpheus-experimental/blob/branch-24.06/dga-detection/datasets/dga-training-data.csv ### Is the model used in an application with physical safety impact? diff --git a/gnn-asset-clustering/README.md b/gnn-asset-clustering/README.md index e415bb3..c182367 100644 --- a/gnn-asset-clustering/README.md +++ b/gnn-asset-clustering/README.md @@ -159,7 +159,7 @@ Armis device and application data
* None ### Link the relevant end user license agreement -* [Apache 2.0](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/LICENSE) +* [Apache 2.0](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/LICENSE) ## Model Card ++ Saftey & Security Subcard diff --git a/ids-detection/README.md b/ids-detection/README.md index 00e9a9f..31b686c 100644 --- a/ids-detection/README.md +++ b/ids-detection/README.md @@ -157,7 +157,7 @@ variance = 0.99 * None ### Link the relevant end user license agreement -* [Apache 2.0](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/LICENSE) +* [Apache 2.0](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/LICENSE) ## Model Card ++ Saftey & Security Subcard diff --git a/log-sequence-ad/README.md b/log-sequence-ad/README.md index b763c64..d9b8040 100644 --- a/log-sequence-ad/README.md +++ b/log-sequence-ad/README.md @@ -175,7 +175,7 @@ Binary classifier output is assigned to each sequence log message in the input f ### What training is recommended for developers working with this model? If none, please state "none." * None ### Link the relevant end user license agreement -* [Apache 2.0](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/LICENSE) +* [Apache 2.0](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/LICENSE) ## Model Card ++ Saftey & Security Subcard From 42bd6d9544a6924647dd1b6e26292669638cd9da Mon Sep 17 00:00:00 2001 From: gbatmaz <50459436+gbatmaz@users.noreply.github.com> Date: Fri, 14 Jun 2024 09:25:54 +0100 Subject: [PATCH 3/7] [REVIEW] cyber foundation notebook and scripts (#77) Cyber foundation notebook, model and scripts that can learn from raw cyber logs and generate synthetic logs which can be used for various downstream tasks Authors: - https://github.com/gbatmaz Approvers: - Tad ZeMicheal (https://github.com/tzemicheal) - https://github.com/hsin-c URL: https://github.com/nv-morpheus/morpheus-experimental/pull/77 --- README.md | 2 + cyber-foundation/README.md | 329 ++++ cyber-foundation/dataset/README.md | 15 + .../azure-ad-logs-sample-training-data.json | 1 + .../model/cyber-foundation-model.pt | 3 + cyber-foundation/model/meta.pkl | Bin 0 -> 873 bytes cyber-foundation/model/train.bin | Bin 0 -> 16538982 bytes cyber-foundation/model/val.bin | Bin 0 -> 1837666 bytes cyber-foundation/requirements.txt | 3 + .../training-inference/cyber-foundation.ipynb | 1318 +++++++++++++++++ cyber-foundation/training-inference/model.py | 388 +++++ .../training-inference/prepare.py | 101 ++ cyber-foundation/training-inference/sample.py | 119 ++ cyber-foundation/training-inference/train.py | 343 +++++ 14 files changed, 2622 insertions(+) create mode 100644 cyber-foundation/README.md create mode 100644 cyber-foundation/dataset/README.md create mode 100644 cyber-foundation/dataset/azure-ad-logs-sample-training-data.json create mode 100644 cyber-foundation/model/cyber-foundation-model.pt create mode 100644 cyber-foundation/model/meta.pkl create mode 100644 cyber-foundation/model/train.bin create mode 100644 cyber-foundation/model/val.bin create mode 100644 cyber-foundation/requirements.txt create mode 100644 cyber-foundation/training-inference/cyber-foundation.ipynb create mode 100644 cyber-foundation/training-inference/model.py create mode 100644 cyber-foundation/training-inference/prepare.py create mode 100644 cyber-foundation/training-inference/sample.py create mode 100644 cyber-foundation/training-inference/train.py diff --git a/README.md b/README.md index 7e60e42..1f080a6 100644 --- a/README.md +++ b/README.md @@ -82,6 +82,8 @@ This model is an XGBoost classifier that predicts each event on a power system b ## [Intrusion Detection System using LODA algorithm](/ids-detection) The model is a Loda anomaly detector for detecting an intrusion attack in the form of bots in a network using a netflow dataset. +## [Cyber Foundation Models](/cyber-foundation) +This model is a GPT that generates realistic synthetic raw Azure AD logs. # Repo Structure Each prototype has its own directory that contains everything belonging to the specific prototype. Directories can include the following subfolders and documentation: diff --git a/cyber-foundation/README.md b/cyber-foundation/README.md new file mode 100644 index 0000000..072f2e1 --- /dev/null +++ b/cyber-foundation/README.md @@ -0,0 +1,329 @@ + + + +# Cyber Foundation + +# Model Overview + +## Description: +* This model is a GPT model trained to generate synthetic Azure logs. This approach can be used to generate logs that are realistic for some downstream tasks, i.e. generating training data as a baseline, generating attack behavior to test detectors.
+ +## Requirements: + +* To run this example, additional requirements must be installed into your environment. A supplementary requirements file has been provided in this example directory. + +`pip install -r requirements.txt` + +## References(s):
+ +* https://github.com/karpathy/nanoGPT
+ +## Model Architecture:
+ +**Architecture Type:**
+ +* Transformer
+ +**Network Architecture:**
+ +* GPT
+ +## Input: (Enter "None" As Needed)
+ +**Input Format:**
+ +* JSON
+ +**Input Parameters:**
+ +* Azure AD Logs
+ +**Other Properties Related to Output:**
+ +* N/A
+ +## Output: (Enter "None" As Needed)
+ +**Output Format:**
+ +* Text file with synthetic logs
+ +**Output Parameters:**
+ +* N/A
+ +**Other Properties Related to Output:** + +* N/A
+ +## Software Integration:
+ +**Runtime(s):**
+ +* Morpheus
+ +**Supported Hardware Platform(s):**
+ +* Ampere/Turing
+ +**Supported Operating System(s):**
+ +* Linux
+ +## Model Version(s): + +* v1
+ +# Training & Evaluation: + +## Training Dataset: + +**Link:**
+ +* https://github.com/nv-morpheus/Morpheus/blob/main/models/datasets/training-data/azure/azure-ad-logs-sample-training-data.json
+ +**Properties (Quantity, Dataset Descriptions, Sensor(s)):**
+ +* 3239 Azure AD logs
+ +**Dataset License:**
+ +* [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
+ +## Evaluation Dataset:
+ +**Link:**
+ +* N/A
+ +**Properties (Quantity, Dataset Descriptions, Sensor(s)):** + +* N/A
+ +**Dataset License:**
+ +* N/A
+ +## Inference:
+ +**Engine:**
+ +* N/A
+ +**Test Hardware:**
+ +* A100
+ +# Subcards + +## Model Card ++ Bias Subcard + +### What is the gender balance of the model validation data? + +* Not Applicable + +### What is the racial/ethnicity balance of the model validation data? + +* Not Applicable + +### What is the age balance of the model validation data? + +* Not Applicable + +### What is the language balance of the model validation data? + +* English: 100% + +### What is the geographic origin language balance of the model validation data? + +* Not Applicable + +### What is the educational background balance of the model validation data? + +* Not Applicable + +### What is the accent balance of the model validation data? + +* Not Applicable + +### What is the face/key point balance of the model validation data? + +* Not Applicable + +### What is the skin/tone balance of the model validation data? + +* Not Applicable + +### What is the religion balance of the model validation data? + +* Not Applicable + +### Individuals from the following adversely impacted (protected classes) groups participate in model design and testing. + +* Not Applicable + +### Describe measures taken to mitigate against unwanted bias. + +* Not Applicable + +## Model Card ++ Explainability Subcard + +### Name example applications and use cases for this model. + +* The model is primarily designed for testing purposes and serves as a small pre-trained model used to generate Azure AD logs. + +### Fill in the blank for the model technique. + +* This model is intended for developers who want to build GPT based synthetic log generator + +### Name who is intended to benefit from this model. + +* The intended beneficiaries of this model are developers who aim to generate synthetic Azure logs. + +### Describe the model output. + +* This model output is synthetic Azure AD logs. + +### List the steps explaining how this model works. + +* This model is an example of a GPT model. This model requires raw log messages as input for training and a prompt for inference. The model is trained as in the training notebook. During inference, the trained model is prompted with the first key of the log type and generates synthetic logs. + +### Name the adversely impacted groups (protected classes) this has been tested to deliver comparable outcomes regardless of: + +* Not Applicable + +### List the technical limitations of the model. + +* This model is trained with synthetic logs for demonstration purposes. A separate training is needed for other logs. + +### What performance metrics were used to affirm the model's performance? + +* Intact raw logs + +### What are the potential known risks to users and stakeholders? + +* N/A + +### What training is recommended for developers working with this model? + +* None + +### Link the relevant end user license agreement + +* [Apache 2.0](http://www.apache.org/licenses/LICENSE-2.0)
+ + +## Model Card ++ Saftey & Security Subcard + +### Link the location of the training dataset's repository. + +* https://github.com/nv-morpheus/Morpheus/blob/main/models/datasets/training-data/azure/azure-ad-logs-sample-training-data.json + +### Is the model used in an application with physical safety impact? + +* No + +### Describe physical safety impact (if present). + +* N/A + +### Was model and dataset assessed for vulnerability for potential form of attack? + +* No + +### Name applications for the model. + +* This model is provided as an example of synthetic log generation. Users can create their own models for their use cases and downstream tasks. + +### Name use case restrictions for the model. + +* It's been trained with a small dataset for mainly demonstration purposes. + +### Has this been verified to have met prescribed quality standards? + +* No + +### Name target quality Key Performance Indicators (KPIs) for which this has been tested. + +* N/A + +### Technical robustness and model security validated? + +* No + +### Is the model and dataset compliant with National Classification Management Society (NCMS)? + +* No + +### Are there explicit model and dataset restrictions? + +* No + +### Are there access restrictions to systems, model, and data? + +* No + +### Is there a digital signature? + +* No + +## Model Card ++ Privacy Subcard + +### Generatable or reverse engineerable personally-identifiable information (PII)? + +* Neither + +### Was consent obtained for any PII used? + +* N/A + +### Protected classes used to create this model? (The following were used in model the model's training:) + +* N/A + +### How often is dataset reviewed? + +* The dataset is initially reviewed upon addition, and subsequent reviews are conducted as needed or upon request for any changes. + +### Is a mechanism in place to honor data subject right of access or deletion of personal data? + +* N/A + +### If PII collected for the development of this AI model, was it minimized to only what was required? + +* N/A + +### Is data in dataset traceable? + +* N/A + +### Scanned for malware? + +* No + +### Are we able to identify and trace source of dataset? + +* Yes + +### Does data labeling (annotation, metadata) comply with privacy laws? + +* N/A + +### Is data compliant with data subject requests for data correction or removal, if such a request was made? + +* N/A diff --git a/cyber-foundation/dataset/README.md b/cyber-foundation/dataset/README.md new file mode 100644 index 0000000..48d8d96 --- /dev/null +++ b/cyber-foundation/dataset/README.md @@ -0,0 +1,15 @@ +## Cyber Foundation Model Data + +### Cyber Foundation Raw Azure AD Logs +This is a synthetic dataset of Azure AD logs with activities of 20 accounts (85 applications involved, 3567 records in total). The activities are split to a train and an inference set. An anomaly is included in the inference set for model validation. The data was generated using the python [faker](https://faker.readthedocs.io/en/master/#) package. If there is any resemblance to real individuals, it is purely coincidental. + +#### Sample Training Data +- 3239 records in total +- Time range: 2022/08/01 - 2022/08/29 +- Users' log distribution: + - 5 high volume (>= 300) users + - 15 medium volume (~100) users + - 5 light volume (~10) users + +- [./azure-ad-logs-sample-training-data.json](./azure-ad-logs-sample-training-data.json) +- [Original location of the training data](https://github.com/nv-morpheus/Morpheus/blob/main/models/datasets/training-data/azure/azure-ad-logs-sample-training-data.json) \ No newline at end of file diff --git a/cyber-foundation/dataset/azure-ad-logs-sample-training-data.json b/cyber-foundation/dataset/azure-ad-logs-sample-training-data.json new file mode 100644 index 0000000..a9f1b4f --- /dev/null +++ b/cyber-foundation/dataset/azure-ad-logs-sample-training-data.json @@ -0,0 +1 @@ +[{"time": "2022-08-01T00:03:56.207532Z", "resourceId": "/tenants/d3e5a967-5657-4a42-afcc-6106b6c3c299/providers/Microsoft.aadiam", "operationName": "Sign-in activity", "operationVersion": "1.0", "category": "NonInteractiveUserSignInLogs", "tenantId": "d3e5a967-5657-4a42-afcc-6106b6c3c299", "resultType": "50158", "resultSignature": "None", "resultDescription": "External security challenge was not satisfied.", "durationMs": 0, "callerIpAddress": "44.22.19.201", "correlationId": "84ca338d-f4ff-4f34-9f2a-5a6e23f78c0b", "identity": "Thomas Price", "Level": 4, "location": "XN", "properties": {"id": "df70b726-7756-4baa-9a7d-5ac965198e00", "createdDateTime": "2022-08-01T00:03:56.371532Z", "userDisplayName": "Thomas Price", "userPrincipalName": "tprice@domain.com", "userId": "32109ee2-ee4f-4d11-9c38-2556aec0f2b5", "appId": "9a7e67c7-6f05-42a3-b226-97c7ec3e9696", "appDisplayName": "Adobe Identity Management", "ipAddress": "44.22.19.201", "status": {"errorCode": 50158, "failureReason": "External security challenge was not satisfied."}, "clientAppUsed": "Mobile Apps and Desktop clients", "userAgent": "Mozilla/5.0 (X11; Linux i686) AppleWebKit/535.2 (KHTML, like Gecko) Chrome/22.0.843.0 Safari/535.2 Edge/118.12158", "deviceDetail": {"deviceId": "0927e60c-8dfa-4ecf-be85-ad63bccf40a1", "displayName": "THOMASPRICE-LT", "operatingSystem": "Windows 10", "browser": "Edge 118.12158", "trustType": "Hybrid Azure AD joined"}, "location": {"city": "Littlemouth", "state": "Alexanderfurt", "countryOrRegion": "XN", "geoCoordinates": {"latitude": 25.4437255, "longitude": -109.530885}}, "correlationId": "bf37c95d-08e3-4342-b83f-4827b020e5b4", "conditionalAccessStatus": "failure", "appliedConditionalAccessPolicies": [], "authenticationContextClassReferences": [], "originalRequestId": "dce375bd-b82e-494f-98e4-1ad345dda0bf", "isInteractive": false, "tokenIssuerName": "", "tokenIssuerType": "AzureAD", "authenticationProcessingDetails": [], "networkLocationDetails": [], "clientCredentialType": "none", "processingTimeInMilliseconds": 164, 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"rngcStatus": 0}}, {"time": "2022-08-01T00:19:37.909827Z", "resourceId": "/tenants/d3e5a967-5657-4a42-afcc-6106b6c3c299/providers/Microsoft.aadiam", "operationName": "Sign-in activity", "operationVersion": "1.0", "category": "SignInLogs", "tenantId": "d3e5a967-5657-4a42-afcc-6106b6c3c299", "resultType": "0", "resultSignature": "None", "durationMs": 0, "callerIpAddress": "99.116.100.205", "correlationId": "7641103c-1db3-4e14-9ebc-6a9555ba02b2", "identity": "Aaron Cole", "Level": 4, "location": "XD", "properties": {"id": "c98bb980-53fe-43a8-afd2-72b917706b00", "createdDateTime": "2022-08-01T00:19:38.009827Z", "userDisplayName": "Aaron Cole", "userPrincipalName": "acole@domain.com", "userId": "5d557969-1645-4ba4-be83-b6fd943659f7", "appId": "e47a1d38-5f61-45cd-b1b9-bc92f525c598", "appDisplayName": "Altoura", "ipAddress": "99.116.100.205", "status": {"errorCode": 0}, "clientAppUsed": "Browser", "userAgent": "Mozilla/5.0 (Linux; Android 2.3.5) AppleWebKit/532.2 (KHTML, like Gecko) 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"f0630e90-b752-4960-bfb9-a0794fc34930", "resourceTenantId": "d3e5a967-5657-4a42-afcc-6106b6c3c299", "homeTenantId": "d3e5a967-5657-4a42-afcc-6106b6c3c299", "authenticationDetails": [], "authenticationRequirementPolicies": [], "authenticationRequirement": "singleFactorAuthentication", "servicePrincipalId": "", "userType": "Member", "flaggedForReview": false, "isTenantRestricted": false, "autonomousSystemNumber": 214655, "crossTenantAccessType": "none", "privateLinkDetails": {}, "ssoExtensionVersion": "", "uniqueTokenIdentifier": "JmqXv1yjmpLCVKYQkwoaQn0ibst82O1aYzAfql41BAoBA52Q", "incomingTokenType": "none", "authenticationProtocol": "none", "appServicePrincipalId": null, "resourceServicePrincipalId": "1f37c851-4be4-455b-928a-fbde7845a68a", "rngcStatus": 0}}, {"time": "2022-08-01T00:25:38.530749Z", "resourceId": "/tenants/d3e5a967-5657-4a42-afcc-6106b6c3c299/providers/Microsoft.aadiam", "operationName": "Sign-in activity", "operationVersion": "1.0", "category": 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Try out the resolution listed at https://docs.microsoft.com/azure/active-directory/application-sign-in-problem-federated-sso-gallery#the-reply-address-does-not-match-the-reply-addresses-configured-for-the-application. If you still see issues, contact the application owner or app admin.", "durationMs": 0, "callerIpAddress": "205.33.24.92", "correlationId": "cc893523-4ec1-4d05-a3d4-cb5fc6bda849", "identity": "Aaron Cole", "Level": 4, "location": "XD", "properties": {"id": "528a72ae-c612-474f-a22e-2f699a097000", "createdDateTime": "2022-08-08T07:21:26.650289Z", "userDisplayName": "Aaron Cole", "userPrincipalName": "acole@domain.com", "userId": "5d557969-1645-4ba4-be83-b6fd943659f7", "appId": "af50c7b0-ad5f-40ec-840e-8d89be454ce4", "appDisplayName": "Citrix ShareFile", "ipAddress": "205.33.24.92", "status": {"errorCode": 50011, "failureReason": "The reply address is missing, misconfigured, or does not match reply addresses configured for the application. 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"resultDescription": "The reply address is missing, misconfigured, or does not match reply addresses configured for the application. Try out the resolution listed at https://docs.microsoft.com/azure/active-directory/application-sign-in-problem-federated-sso-gallery#the-reply-address-does-not-match-the-reply-addresses-configured-for-the-application. If you still see issues, contact the application owner or app admin.", "durationMs": 0, "callerIpAddress": "205.33.24.92", "correlationId": "289112f6-f632-4ca6-bc72-864847ea8b8c", "identity": "Aaron Cole", "Level": 4, "location": "XD", "properties": {"id": "96843c4e-d6c8-49c6-9d20-6412304b7000", "createdDateTime": "2022-08-20T23:39:40.573553Z", "userDisplayName": "Aaron Cole", "userPrincipalName": "acole@domain.com", "userId": "5d557969-1645-4ba4-be83-b6fd943659f7", "appId": "af50c7b0-ad5f-40ec-840e-8d89be454ce4", "appDisplayName": "Citrix ShareFile", "ipAddress": "205.33.24.92", "status": {"errorCode": 50011, "failureReason": "The reply address is missing, misconfigured, or does not match reply addresses configured for the application. Try out the resolution listed at https://docs.microsoft.com/azure/active-directory/application-sign-in-problem-federated-sso-gallery#the-reply-address-does-not-match-the-reply-addresses-configured-for-the-application. 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zUp7}qqCNI)-s&>;{m#z)RgQHkZlx|%)Qdf4cE`$RZDT#CFmq<9S;`$lvzrmWzsBn@ z(Z1Bt?5pAXUI}lQM|?fj<}aCNiy2sft%wy^0DDceO1hfAO=>+0F|=>iQfgZ>*<@K`J>;wd*?GM3FIHW) znV0{M_|2aC+ZgAOv);{0k6e3*Yx8XBIa7CSUA2~46`gGvg3NJQHF?FURM001IdgUC z9{PUV%dTQ{)=y8LTi==|oyVTzNH$c*{b%ti{Zn4Y?HAoV>1&T_?9YGpbft33Ol?Qj zwO%{aZ6C=MK z`SmP)tI<)h-?V-GG&@14bmtw=YLq9%>RWaCdzSY1aG3t)_5$no9YqjOsEq4&;`Xyx z4dz+@|AY^e@9O<_!z;G`f_1Nm*nB?LuEn!B`oFsM@kYG9*371x`aK@Eb{5;Z6Mpx( zpkwD_WxX2jb|w7sXK~NbSY4mQnRpbB{1A`t#JYDY?z3{ei#;1JhJU&pNBS#li)`PS z;Mb43vvxd=b3U|7m&4{X8!~FP=V`W41gdM?Kus>Ph= 2.0 + self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') + if not self.flash: + print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0") + # causal mask to ensure that attention is only applied to the left in the input sequence + self.register_buffer( + "bias", + torch.tril(torch.ones(config.block_size, config.block_size)).view(1, + 1, + config.block_size, + config.block_size)) + + def forward(self, x): + B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd) + + # calculate query, key, values for all heads in batch and move head forward to be the batch dim + q, k, v = self.c_attn(x).split(self.n_embd, dim=2) + k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) + q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) + v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) + + # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T) + if self.flash: + # efficient attention using Flash Attention CUDA kernels + y = torch.nn.functional.scaled_dot_product_attention(q, + k, + v, + attn_mask=None, + dropout_p=self.dropout if self.training else 0, + is_causal=True) + else: + # manual implementation of attention + att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) + att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf')) + att = F.softmax(att, dim=-1) + att = self.attn_dropout(att) + y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs) + y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side + + # output projection + y = self.resid_dropout(self.c_proj(y)) + return y + + +class MLP(nn.Module): + + def __init__(self, config): + super().__init__() + self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias) + self.gelu = nn.GELU() + self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias) + self.dropout = nn.Dropout(config.dropout) + + def forward(self, x): + x = self.c_fc(x) + x = self.gelu(x) + x = self.c_proj(x) + x = self.dropout(x) + return x + + +class Block(nn.Module): + + def __init__(self, config): + super().__init__() + self.ln_1 = LayerNorm(config.n_embd, bias=config.bias) + self.attn = CausalSelfAttention(config) + self.ln_2 = LayerNorm(config.n_embd, bias=config.bias) + self.mlp = MLP(config) + + def forward(self, x): + x = x + self.attn(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +@dataclass +class GPTConfig: + block_size: int = 1024 + vocab_size: int = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency + n_layer: int = 12 + n_head: int = 12 + n_embd: int = 768 + dropout: float = 0.0 + bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster + + +class GPT(nn.Module): + + def __init__(self, config): + super().__init__() + assert config.vocab_size is not None + assert config.block_size is not None + self.config = config + + self.transformer = nn.ModuleDict( + dict( + wte=nn.Embedding(config.vocab_size, config.n_embd), + wpe=nn.Embedding(config.block_size, config.n_embd), + drop=nn.Dropout(config.dropout), + h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]), + ln_f=LayerNorm(config.n_embd, bias=config.bias), + )) + self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) + # with weight tying when using torch.compile() some warnings get generated: + # "UserWarning: functional_call was passed multiple values for tied weights. + # This behavior is deprecated and will be an error in future versions" + # not 100% sure what this is, so far seems to be harmless. TODO investigate + self.transformer.wte.weight = self.lm_head.weight # https://paperswithcode.com/method/weight-tying + + # init all weights + self.apply(self._init_weights) + # apply special scaled init to the residual projections, per GPT-2 paper + for pn, p in self.named_parameters(): + if pn.endswith('c_proj.weight'): + torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer)) + + # report number of parameters + print("number of parameters: %.2fM" % (self.get_num_params() / 1e6, )) + + def get_num_params(self, non_embedding=True): + """ + Return the number of parameters in the model. + For non-embedding count (default), the position embeddings get subtracted. + The token embeddings would too, except due to the parameter sharing these + params are actually used as weights in the final layer, so we include them. + """ + n_params = sum(p.numel() for p in self.parameters()) + if non_embedding: + n_params -= self.transformer.wpe.weight.numel() + return n_params + + def _init_weights(self, module): + if isinstance(module, nn.Linear): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + if module.bias is not None: + torch.nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + + def forward(self, idx, targets=None): + device = idx.device + b, t = idx.size() + assert t <= self.config.block_size, ( + f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}") + pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t) + + # forward the GPT model itself + tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd) + pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd) + x = self.transformer.drop(tok_emb + pos_emb) + for block in self.transformer.h: + x = block(x) + x = self.transformer.ln_f(x) + + if targets is not None: + # if we are given some desired targets also calculate the loss + logits = self.lm_head(x) + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1) + else: + # inference-time mini-optimization: only forward the lm_head on the very last position + logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim + loss = None + + return logits, loss + + def crop_block_size(self, block_size): + # model surgery to decrease the block size if necessary + # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024) + # but want to use a smaller block size for some smaller, simpler model + assert block_size <= self.config.block_size + self.config.block_size = block_size + self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size]) + for block in self.transformer.h: + if hasattr(block.attn, 'bias'): + block.attn.bias = block.attn.bias[:, :, :block_size, :block_size] + + @classmethod + def from_pretrained(cls, model_type, override_args=None): + assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'} + override_args = override_args or {} # default to empty dict + # only dropout can be overridden see more notes below + assert all(k == 'dropout' for k in override_args) + from transformers import GPT2LMHeadModel + print("loading weights from pretrained gpt: %s" % model_type) + + # n_layer, n_head and n_embd are determined from model_type + config_args = { + 'gpt2': dict(n_layer=12, n_head=12, n_embd=768), # 124M params + 'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024), # 350M params + 'gpt2-large': dict(n_layer=36, n_head=20, n_embd=1280), # 774M params + 'gpt2-xl': dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params + }[model_type] + print("forcing vocab_size=50257, block_size=1024, bias=True") + config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints + config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints + config_args['bias'] = True # always True for GPT model checkpoints + # we can override the dropout rate, if desired + if 'dropout' in override_args: + print(f"overriding dropout rate to {override_args['dropout']}") + config_args['dropout'] = override_args['dropout'] + # create a from-scratch initialized minGPT model + config = GPTConfig(**config_args) + model = GPT(config) + sd = model.state_dict() + sd_keys = sd.keys() + sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')] # discard this mask / buffer, not a param + + # init a huggingface/transformers model + model_hf = GPT2LMHeadModel.from_pretrained(model_type) + sd_hf = model_hf.state_dict() + + # copy while ensuring all of the parameters are aligned and match in names and shapes + sd_keys_hf = sd_hf.keys() + sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')] # ignore these, just a buffer + sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')] # same, just the mask (buffer) + transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight'] + # basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear + # this means that we have to transpose these weights when we import them + assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}" + for k in sd_keys_hf: + if any(k.endswith(w) for w in transposed): + # special treatment for the Conv1D weights we need to transpose + assert sd_hf[k].shape[::-1] == sd[k].shape + with torch.no_grad(): + sd[k].copy_(sd_hf[k].t()) + else: + # vanilla copy over the other parameters + assert sd_hf[k].shape == sd[k].shape + with torch.no_grad(): + sd[k].copy_(sd_hf[k]) + + return model + + def configure_optimizers(self, weight_decay, learning_rate, betas, device_type): + # start with all of the candidate parameters + param_dict = {pn: p for pn, p in self.named_parameters()} + # filter out those that do not require grad + param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad} + # create optim groups. Any parameters that is 2D will be weight decayed, otherwise no. + # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't. + decay_params = [p for n, p in param_dict.items() if p.dim() >= 2] + nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2] + optim_groups = [{ + 'params': decay_params, 'weight_decay': weight_decay + }, { + 'params': nodecay_params, 'weight_decay': 0.0 + }] + num_decay_params = sum(p.numel() for p in decay_params) + num_nodecay_params = sum(p.numel() for p in nodecay_params) + print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters") + print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters") + # Create AdamW optimizer and use the fused version if it is available + fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters + use_fused = fused_available and device_type == 'cuda' + extra_args = dict(fused=True) if use_fused else dict() + optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args) + print(f"using fused AdamW: {use_fused}") + + return optimizer + + def estimate_mfu(self, fwdbwd_per_iter, dt): + """ estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS """ + # first estimate the number of flops we do per iteration. + # see PaLM paper Appendix B as ref: https://arxiv.org/abs/2204.02311 + N = self.get_num_params() + cfg = self.config + L, H, Q, T = cfg.n_layer, cfg.n_head, cfg.n_embd // cfg.n_head, cfg.block_size + flops_per_token = 6 * N + 12 * L * H * Q * T + flops_per_fwdbwd = flops_per_token * T + flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter + # express our flops throughput as ratio of A100 bfloat16 peak flops + flops_achieved = flops_per_iter * (1.0 / dt) # per second + flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS + mfu = flops_achieved / flops_promised + return mfu + + @torch.no_grad() + def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None): + """ + Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete + the sequence max_new_tokens times, feeding the predictions back into the model each time. + Most likely you'll want to make sure to be in model.eval() mode of operation for this. + """ + for _ in range(max_new_tokens): + # if the sequence context is growing too long we must crop it at block_size + idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:] + # forward the model to get the logits for the index in the sequence + logits, _ = self(idx_cond) + # pluck the logits at the final step and scale by desired temperature + logits = logits[:, -1, :] / temperature + # optionally crop the logits to only the top k options + if top_k is not None: + v, _ = torch.topk(logits, min(top_k, logits.size(-1))) + logits[logits < v[:, [-1]]] = -float('Inf') + # apply softmax to convert logits to (normalized) probabilities + probs = F.softmax(logits, dim=-1) + # sample from the distribution + idx_next = torch.multinomial(probs, num_samples=1) + # append sampled index to the running sequence and continue + idx = torch.cat((idx, idx_next), dim=1) + + return idx diff --git a/cyber-foundation/training-inference/prepare.py b/cyber-foundation/training-inference/prepare.py new file mode 100644 index 0000000..a64777f --- /dev/null +++ b/cyber-foundation/training-inference/prepare.py @@ -0,0 +1,101 @@ +# SPDX-FileCopyrightText: Copyright (c) 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. + +# The base code is taken from https://github.com/karpathy/nanoGPT + +# The license on the original repository is below + +# MIT License + +# Copyright (c) 2022 Andrej Karpathy + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import json +import os +import pickle + +import numpy as np + +with open('../dataset/azure-ad-logs-sample-training-data.json', 'r') as file: + data = json.load(file) + +json_strings = [json.dumps(dictionary) for dictionary in data] +combined_json_string = '\n'.join(json_strings) + +data = combined_json_string + +# get all the unique characters that occur in this text +chars = sorted(list(set(data))) +vocab_size = len(chars) +print("all the unique characters:", ''.join(chars)) +print(f"vocab size: {vocab_size:,}") + +# create a mapping from characters to integers +stoi = {ch: i for i, ch in enumerate(chars)} +itos = {i: ch for i, ch in enumerate(chars)} + + +def encode(s): + # encoder: take a string, output a list of integers + return [stoi[c] for c in s] + + +def decode(sequence): + # decoder: take a list of integers, output a string + return ''.join([itos[i] for i in sequence]) + + +# create the train and test splits +n = len(data) +train_data = data[:int(n * 0.9)] +val_data = data[int(n * 0.9):] + +# encode both to integers +train_ids = encode(train_data) +val_ids = encode(val_data) +print(f"train has {len(train_ids):,} tokens") +print(f"val has {len(val_ids):,} tokens") + +# export to bin files +train_ids = np.array(train_ids, dtype=np.uint16) +val_ids = np.array(val_ids, dtype=np.uint16) +train_ids.tofile(os.path.join(os.path.dirname(__file__), '../model/train.bin')) +val_ids.tofile(os.path.join(os.path.dirname(__file__), '../model/val.bin')) + +# save the meta information as well, to help us encode/decode later +meta = { + 'vocab_size': vocab_size, + 'itos': itos, + 'stoi': stoi, +} +with open(os.path.join(os.path.dirname(__file__), '../model/meta.pkl'), 'wb') as f: + pickle.dump(meta, f) diff --git a/cyber-foundation/training-inference/sample.py b/cyber-foundation/training-inference/sample.py new file mode 100644 index 0000000..0af28f0 --- /dev/null +++ b/cyber-foundation/training-inference/sample.py @@ -0,0 +1,119 @@ +# SPDX-FileCopyrightText: Copyright (c) 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. + +# The base code is taken from https://github.com/karpathy/nanoGPT + +# The license on the original repository is below + +# MIT License + +# Copyright (c) 2022 Andrej Karpathy + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import os +import pickle +import sys +from contextlib import nullcontext + +import torch +from model import GPT +from model import GPTConfig + +init_from = 'resume' # either 'resume' (from an out_dir) or a gpt2 variant (e.g. 'gpt2-xl') +out_dir = '../model/' # ignored if init_from is not 'resume' +start = sys.argv[1] # or "" or etc. Can also specify a file, use as: "FILE:prompt.txt" +num_samples = 10 # number of samples to draw +max_new_tokens = 5000 # number of tokens generated in each sample +temperature = 0.8 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions +top_k = 200 # retain only the top_k most likely tokens, clamp others to have 0 probability +seed = 1337 +device = 'cuda' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc. +dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported( +) else 'float16' # 'float32' or 'bfloat16' or 'float16' +compile = False # use PyTorch 2.0 to compile the model to be faster + +torch.manual_seed(seed) +torch.cuda.manual_seed(seed) +torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul +torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn +device_type = 'cuda' if 'cuda' in device else 'cpu' # for later use in torch.autocast +ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype] +ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype) + +# model +if init_from == 'resume': + # init from a model saved in a specific directory + ckpt_path = os.path.join(out_dir, '../model/cyber-foundation-model.pt') + checkpoint = torch.load(ckpt_path, map_location=device) + gptconf = GPTConfig(**checkpoint['model_args']) + model = GPT(gptconf) + state_dict = checkpoint['model'] + unwanted_prefix = '_orig_mod.' + for k, v in list(state_dict.items()): + if k.startswith(unwanted_prefix): + state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) + model.load_state_dict(state_dict) +elif init_from.startswith('gpt2'): + # init from a given GPT-2 model + model = GPT.from_pretrained(init_from, dict(dropout=0.0)) + +model.eval() +model.to(device) +if compile: + model = torch.compile(model) # requires PyTorch 2.0 (optional) + +with open("../model/meta.pkl", 'rb') as f: + meta = pickle.load(f) +# TODO want to make this more general to arbitrary encoder/decoder schemes +stoi, itos = meta['stoi'], meta['itos'] + + +def encode(s): + return [stoi[c] for c in s] + + +def decode(sequence): + return ''.join([itos[i] for i in sequence]) + + +start_ids = encode(start) + +x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...]) + +# run generation +with torch.no_grad(): + with ctx: + for k in range(num_samples): + y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k) + decoded_text = decode(y[0].tolist()) + print(decoded_text) + print('---------------') diff --git a/cyber-foundation/training-inference/train.py b/cyber-foundation/training-inference/train.py new file mode 100644 index 0000000..d1ffca1 --- /dev/null +++ b/cyber-foundation/training-inference/train.py @@ -0,0 +1,343 @@ +# SPDX-FileCopyrightText: Copyright (c) 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. + +# The base code is taken from https://github.com/karpathy/nanoGPT + +# The license on the original repository is below + +# MIT License + +# Copyright (c) 2022 Andrej Karpathy + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import math +import os +import pickle +import time +from contextlib import nullcontext + +import numpy as np +import torch +from model import GPT +from model import GPTConfig +from torch.distributed import destroy_process_group +from torch.distributed import init_process_group +from torch.nn.parallel import DistributedDataParallel as DDP + +out_dir = '../model/' +eval_interval = 100 +log_interval = 1 +eval_iters = 20 +eval_only = False # if True, script exits right after the first eval +always_save_checkpoint = True # if True, always save a checkpoint after each eval +init_from = 'scratch' # 'scratch' or 'resume' or 'gpt2*' +# data +data_dir = '../model/' +gradient_accumulation_steps = 20 +batch_size = 12 # if gradient_accumulation_steps > 1, this is the micro-batch size +block_size = 2048 +# model +n_layer = 12 +n_head = 12 +n_embd = 768 +dropout = 0.0 # for pretraining 0 is good, for finetuning try 0.1+ +bias = False # do we use bias inside LayerNorm and Linear layers? +# adamw optimizer +learning_rate = 6e-4 # max learning rate +max_iters = 1000 # total number of training iterations +weight_decay = 1e-1 +beta1 = 0.9 +beta2 = 0.95 +grad_clip = 1.0 # clip gradients at this value, or disable if == 0.0 +# learning rate decay settings +decay_lr = True # whether to decay the learning rate +warmup_iters = 2000 # how many steps to warm up for +lr_decay_iters = 100 # should be ~= max_iters per Chinchilla +min_lr = 6e-5 # minimum learning rate, should be ~= learning_rate/10 per Chinchilla +# DDP settings +backend = 'nccl' # 'nccl', 'gloo', etc. +# system +device = 'cuda' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1' etc., or try 'mps' on macbooks +dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported( +) else 'float16' # 'float32', 'bfloat16', or 'float16', the latter will auto implement a GradScaler +compile = True # use PyTorch 2.0 to compile the model to be faster + +# various inits, derived attributes, I/O setup +ddp = int(os.environ.get('RANK', -1)) != -1 # is this a ddp run? +if ddp: + init_process_group(backend=backend) + ddp_rank = int(os.environ['RANK']) + ddp_local_rank = int(os.environ['LOCAL_RANK']) + ddp_world_size = int(os.environ['WORLD_SIZE']) + device = f'cuda:{ddp_local_rank}' + torch.cuda.set_device(device) + master_process = ddp_rank == 0 # this process will do logging, checkpointing etc. + seed_offset = ddp_rank # each process gets a different seed + # world_size number of processes will be training simultaneously, so we can scale + # down the desired gradient accumulation iterations per process proportionally + assert gradient_accumulation_steps % ddp_world_size == 0 + gradient_accumulation_steps //= ddp_world_size +else: + # if not ddp, we are running on a single gpu, and one process + master_process = True + seed_offset = 0 + ddp_world_size = 1 +tokens_per_iter = gradient_accumulation_steps * ddp_world_size * batch_size * block_size +print(f"tokens per iteration will be: {tokens_per_iter:,}") + +if master_process: + os.makedirs(out_dir, exist_ok=True) +torch.manual_seed(1337 + seed_offset) +torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul +torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn +device_type = 'cuda' if 'cuda' in device else 'cpu' # for later use in torch.autocast +# note: float16 data type will automatically use a GradScaler +ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype] +ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype) + + +def get_batch(split): + # We recreate np.memmap every batch to avoid a memory leak, as per + # https://stackoverflow.com/questions/45132940/numpy-memmap-memory-usage-want-to-iterate-once/61472122#61472122 + if split == 'train': + data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r') + else: + data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r') + ix = torch.randint(len(data) - block_size, (batch_size, )) + x = torch.stack([torch.from_numpy((data[i:i + block_size]).astype(np.int64)) for i in ix]) + y = torch.stack([torch.from_numpy((data[i + 1:i + 1 + block_size]).astype(np.int64)) for i in ix]) + if device_type == 'cuda': + # pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True) + x, y = x.pin_memory().to(device, non_blocking=True), y.pin_memory().to(device, non_blocking=True) + else: + x, y = x.to(device), y.to(device) + return x, y + + +# init these up here, can override if init_from='resume' (i.e. from a checkpoint) +iter_num = 0 +best_val_loss = 1e9 + +meta_path = os.path.join(out_dir, 'meta.pkl') +meta_vocab_size = None +if os.path.exists(meta_path): + with open(meta_path, 'rb') as f: + meta = pickle.load(f) + meta_vocab_size = meta['vocab_size'] + print(f"found vocab_size = {meta_vocab_size} (inside {meta_path})") + +# model init +model_args = dict(n_layer=n_layer, + n_head=n_head, + n_embd=n_embd, + block_size=block_size, + bias=bias, + vocab_size=None, + dropout=dropout) # start with model_args from command line +if init_from == 'scratch': + # init a new model from scratch + print("Initializing a new model from scratch") + # determine the vocab size we'll use for from-scratch training + if meta_vocab_size is None: + print("defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency)") + model_args['vocab_size'] = meta_vocab_size if meta_vocab_size is not None else 50304 + gptconf = GPTConfig(**model_args) + model = GPT(gptconf) +elif init_from == 'resume': + print(f"Resuming training from {out_dir}") + # resume training from a checkpoint. + ckpt_path = os.path.join(out_dir, 'cyber-foundation-model.pt') + checkpoint = torch.load(ckpt_path, map_location=device) + checkpoint_model_args = checkpoint['model_args'] + # force these config attributes to be equal otherwise we can't even resume training + # the rest of the attributes (e.g. dropout) can stay as desired from command line + for k in ['n_layer', 'n_head', 'n_embd', 'block_size', 'bias', 'vocab_size']: + model_args[k] = checkpoint_model_args[k] + # create the model + gptconf = GPTConfig(**model_args) + model = GPT(gptconf) + state_dict = checkpoint['model'] + # fix the keys of the state dictionary :( + # honestly no idea how checkpoints sometimes get this prefix, have to debug more + unwanted_prefix = '_orig_mod.' + for k, v in list(state_dict.items()): + if k.startswith(unwanted_prefix): + state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k) + model.load_state_dict(state_dict) + iter_num = checkpoint['iter_num'] + best_val_loss = checkpoint['best_val_loss'] +elif init_from.startswith('gpt2'): + print(f"Initializing from OpenAI GPT-2 weights: {init_from}") + # initialize from OpenAI GPT-2 weights + override_args = dict(dropout=dropout) + model = GPT.from_pretrained(init_from, override_args) + # read off the created config params, so we can store them into checkpoint correctly + for k in ['n_layer', 'n_head', 'n_embd', 'block_size', 'bias', 'vocab_size']: + model_args[k] = getattr(model.config, k) +# crop down the model block size if desired, using model surgery +if block_size < model.config.block_size: + model.crop_block_size(block_size) + model_args['block_size'] = block_size # so that the checkpoint will have the right value +model.to(device) + +# initialize a GradScaler. If enabled=False scaler is a no-op +scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16')) + +# optimizer +optimizer = model.configure_optimizers(weight_decay, learning_rate, (beta1, beta2), device_type) +if init_from == 'resume': + optimizer.load_state_dict(checkpoint['optimizer']) +checkpoint = None # free up memory + +# compile the model +if compile: + print("compiling the model... (takes a ~minute)") + unoptimized_model = model + model = torch.compile(model) # requires PyTorch 2.0 + +# wrap model into DDP container +if ddp: + model = DDP(model, device_ids=[ddp_local_rank]) + + +# helps estimate an arbitrarily accurate loss over either split using many batches +@torch.no_grad() +def estimate_loss(): + out = {} + model.eval() + for split in ['train', 'val']: + losses = torch.zeros(eval_iters) + for k in range(eval_iters): + X, Y = get_batch(split) + with ctx: + logits, loss = model(X, Y) + losses[k] = loss.item() + out[split] = losses.mean() + model.train() + return out + + +def get_lr(it): + # learning rate decay scheduler (cosine with warmup) + # 1) linear warmup for warmup_iters steps + if it < warmup_iters: + return learning_rate * it / warmup_iters + # 2) if it > lr_decay_iters, return min learning rate + if it > lr_decay_iters: + return min_lr + # 3) in between, use cosine decay down to min learning rate + decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters) + assert 0 <= decay_ratio <= 1 + coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff ranges 0..1 + return min_lr + coeff * (learning_rate - min_lr) + + +# training loop +X, Y = get_batch('train') # fetch the very first batch +t0 = time.time() +local_iter_num = 0 # number of iterations in the lifetime of this process +raw_model = model.module if ddp else model # unwrap DDP container if needed +running_mfu = -1.0 +while True: + + # determine and set the learning rate for this iteration + lr = get_lr(iter_num) if decay_lr else learning_rate + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + # evaluate the loss on train/val sets and write checkpoints + if iter_num % eval_interval == 0 and master_process: + losses = estimate_loss() + print(f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}") + + if losses['val'] < best_val_loss or always_save_checkpoint: + best_val_loss = losses['val'] + if iter_num > 0: + checkpoint = { + 'model': raw_model.state_dict(), + 'optimizer': optimizer.state_dict(), + 'model_args': model_args, + 'iter_num': iter_num, + 'best_val_loss': best_val_loss, # 'config': config, + } + print(f"saving checkpoint to {out_dir}") + torch.save(checkpoint, os.path.join(out_dir, 'cyber-foundation-model.pt')) + if iter_num == 0 and eval_only: + break + + # forward backward update, with optional gradient accumulation to simulate larger batch size + # and using the GradScaler if data type is float16 + for micro_step in range(gradient_accumulation_steps): + if ddp: + # in DDP training we only need to sync gradients at the last micro step. + # the official way to do this is with model.no_sync() context manager, but + # I really dislike that this bloats the code and forces us to repeat code + # looking at the source of that context manager, it just toggles this variable + model.require_backward_grad_sync = (micro_step == gradient_accumulation_steps - 1) + with ctx: + logits, loss = model(X, Y) + loss = loss / gradient_accumulation_steps # scale the loss to account for gradient accumulation + # immediately async prefetch next batch while model is doing the forward pass on the GPU + X, Y = get_batch('train') + # backward pass, with gradient scaling if training in fp16 + scaler.scale(loss).backward() + # clip the gradient + if grad_clip != 0.0: + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip) + # step the optimizer and scaler if training in fp16 + scaler.step(optimizer) + scaler.update() + # flush the gradients as soon as we can, no need for this memory anymore + optimizer.zero_grad(set_to_none=True) + + # timing and logging + t1 = time.time() + dt = t1 - t0 + t0 = t1 + if iter_num % log_interval == 0 and master_process: + # get loss as float. note: this is a CPU-GPU sync point + # scale up to undo the division above, approximating the true total loss (exact would have been a sum) + lossf = loss.item() * gradient_accumulation_steps + if local_iter_num >= 5: # let the training loop settle a bit + mfu = raw_model.estimate_mfu(batch_size * gradient_accumulation_steps, dt) + running_mfu = mfu if running_mfu == -1.0 else 0.9 * running_mfu + 0.1 * mfu + print(f"iter {iter_num}: loss {lossf:.4f}, time {dt*1000:.2f}ms, mfu {running_mfu*100:.2f}%") + iter_num += 1 + local_iter_num += 1 + + # termination conditions + if iter_num > max_iters: + break + +if ddp: + destroy_process_group() From 676d25a9428068e1275c9bf7c5ba0c1e2dbf3076 Mon Sep 17 00:00:00 2001 From: Tad ZeMicheal Date: Mon, 1 Jul 2024 16:09:50 -0500 Subject: [PATCH 4/7] update nltk.bigrams & remove nltk dependency --- log-sequence-ad/requirements.txt | 1 - .../training-tuning-inference/datatools.py | 34 ++++++++++++++++++- 2 files changed, 33 insertions(+), 2 deletions(-) diff --git a/log-sequence-ad/requirements.txt b/log-sequence-ad/requirements.txt index bf645b3..ae53995 100644 --- a/log-sequence-ad/requirements.txt +++ b/log-sequence-ad/requirements.txt @@ -1,5 +1,4 @@ --find-links https://download.pytorch.org/whl/cu118 gensim==3.8.0 -nltk==3.8 torch==2.0.1 diff --git a/log-sequence-ad/training-tuning-inference/datatools.py b/log-sequence-ad/training-tuning-inference/datatools.py index 0f67acb..8442016 100644 --- a/log-sequence-ad/training-tuning-inference/datatools.py +++ b/log-sequence-ad/training-tuning-inference/datatools.py @@ -19,16 +19,48 @@ import random import warnings +from collections import deque +from itertools import islice import numpy as np import pandas as pd import torch from gensim.models import Word2Vec -from nltk.util import bigrams warnings.filterwarnings("ignore") +def ngrams(sequence, n, **kwargs): + """compute ngram. This method is based on nltk.util.ngrams implementation. + + Parameters + ---------- + sequence : list + List of strings + n : int + ngram param. set n=2 for bigram + + Yields + ------ + _type_ + _description_ + """ + + # sliding_window('ABCDEFG', 4) --> ABCD BCDE CDEF DEFG + # https://docs.python.org/3/library/itertools.html?highlight=sliding_window#itertools-recipes + it = iter(sequence) + window = deque(islice(it, n), maxlen=n) + if len(window) == n: + yield tuple(window) + for x in it: + window.append(x) + yield tuple(window) + + +def bigrams(sequence, **kwargs): + yield from ngrams(sequence, 2, **kwargs) + + def preprocess(df, window_size=100, step_size=20): '''Preprocessing structured log dataset From 43ef2c3b3468a38e90f0efa058f16d080822972e Mon Sep 17 00:00:00 2001 From: Tad ZeMicheal Date: Mon, 1 Jul 2024 21:31:58 -0500 Subject: [PATCH 5/7] copyright fix --- log-sequence-ad/training-tuning-inference/datatools.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/log-sequence-ad/training-tuning-inference/datatools.py b/log-sequence-ad/training-tuning-inference/datatools.py index 8442016..3a92b77 100644 --- a/log-sequence-ad/training-tuning-inference/datatools.py +++ b/log-sequence-ad/training-tuning-inference/datatools.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2023-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); From b345ab33744f8ffd6927f2fbe131b3120612cc86 Mon Sep 17 00:00:00 2001 From: David Gardner Date: Tue, 2 Jul 2024 14:17:41 -0700 Subject: [PATCH 6/7] Update CR year --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 39124fa..4631284 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,5 @@ +# morpheus-experimental 24.06.00 (02 Jul 2024) +## 🚀 New Features + +- [REVIEW] cyber foundation notebook and scripts ([#77](https://github.com/nv-morpheus/morpheus-experimental/pull/77)) [@gbatmaz](https://github.com/gbatmaz) + +## 🛠️ Improvements + +- Fix nltk.bigrams & remove nltk dependency alert ([#80](https://github.com/nv-morpheus/morpheus-experimental/pull/80)) [@tzemicheal](https://github.com/tzemicheal) + # morpheus-experimental 24.03.00 (27 Mar 2024) ## 🛠️ Improvements

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