[WIP] Phi3poc #2301

JessicaXYWang · 2024-10-15T06:12:05Z

Related Issues/PRs

#xxx

What changes are proposed in this pull request?

Briefly describe the changes included in this Pull Request.

How is this patch tested?

I have written tests (not required for typo or doc fix) and confirmed the proposed feature/bug-fix/change works.

Does this PR change any dependencies?

No. You can skip this section.
Yes. Make sure the dependencies are resolved correctly, and list changes here.

Does this PR add a new feature? If so, have you added samples on website?

No. You can skip this section.
Yes. Make sure you have added samples following below steps.

Find the corresponding markdown file for your new feature in website/docs/documentation folder.
Make sure you choose the correct class estimators/transformers and namespace.
Follow the pattern in markdown file and add another section for your new API, including pyspark, scala (and .NET potentially) samples.
Make sure the DocTable points to correct API link.
Navigate to website folder, and run yarn run start to make sure the website renders correctly.
Don't forget to add  before each python code blocks to enable auto-tests for python samples.
Make sure the WebsiteSamplesTests job pass in the pipeline.

JessicaXYWang · 2024-10-15T06:12:29Z

/azp run

azure-pipelines · 2024-10-15T06:12:39Z

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codecov-commenter · 2024-10-15T06:24:19Z

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 84.53%. Comparing base (bab6aed) to head (7a3e315).

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #2301      +/-   ##
==========================================
- Coverage   84.55%   84.53%   -0.03%     
==========================================
  Files         328      328              
  Lines       16848    16848              
  Branches     1513     1513              
==========================================
- Hits        14246    14242       -4     
- Misses       2602     2606       +4

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…into phi3poc

JessicaXYWang · 2024-10-15T15:03:21Z

/azp run

azure-pipelines · 2024-10-15T15:03:34Z

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JessicaXYWang · 2024-10-17T22:04:34Z

/azp run

azure-pipelines · 2024-10-17T22:04:45Z

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JessicaXYWang · 2024-10-24T17:32:10Z

/azp run

azure-pipelines · 2024-10-24T17:32:23Z

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JessicaXYWang · 2025-01-02T16:13:34Z

/azp run

azure-pipelines · 2025-01-02T16:13:47Z

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core/src/main/python/synapse/ml/llm/HuggingFaceCausallmTransform.py

mhamilton723 · 2025-01-02T17:38:36Z

core/src/main/python/synapse/ml/llm/HuggingFaceCausallmTransform.py

+        self.config.update(kwargs)
+
+
+def camel_to_snake(text):


there might already be one in library to use

mhamilton723 · 2025-01-02T17:39:06Z

core/src/main/python/synapse/ml/llm/HuggingFaceCausallmTransform.py

+        "output column",
+        typeConverter=TypeConverters.toString,
+    )
+    modelParam = Param(Params._dummy(), "modelParam", "Model Parameters")


Maybe explain difference between model params and other params (you can just link to other docs if easier)

mhamilton723 · 2025-01-02T17:39:22Z

core/src/main/python/synapse/ml/llm/HuggingFaceCausallmTransform.py

+        typeConverter=TypeConverters.toString,
+    )
+    modelParam = Param(Params._dummy(), "modelParam", "Model Parameters")
+    modelConfig = Param(Params._dummy(), "modelConfig", "Model configuration")


Maybe explain difference between model config and other params (you can just link to other docs if easier)

mhamilton723 · 2025-01-02T17:39:39Z

core/src/main/python/synapse/ml/llm/HuggingFaceCausallmTransform.py

+    useFabricLakehouse = Param(
+        Params._dummy(),
+        "useFabricLakehouse",
+        "Use FabricLakehouse",


If this is for a local cache then you might be able to make the verbage generic like useLocalCache

JessicaXYWang · 2025-01-14T22:37:37Z

/azp run

azure-pipelines · 2025-01-14T22:37:48Z

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JessicaXYWang · 2025-01-16T05:32:23Z

/azp run

azure-pipelines · 2025-01-16T05:32:36Z

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JessicaXYWang · 2025-01-16T08:35:15Z

/azp run

azure-pipelines · 2025-01-16T08:35:27Z

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JessicaXYWang · 2025-01-16T09:36:48Z

/azp run

azure-pipelines · 2025-01-16T09:36:59Z

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JessicaXYWang · 2025-01-16T16:19:42Z

/azp run

azure-pipelines · 2025-01-16T16:19:54Z

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JessicaXYWang · 2025-01-16T17:30:19Z

/azp run

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JessicaXYWang · 2025-01-16T18:30:00Z

/azp run

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mhamilton723 · 2025-01-16T19:49:55Z

...ore Algorithms/Language Model/Quickstart - Apply Phi 3 Model with HuggingFace CausalLM.ipynb

@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","source":["# Apply Phi3 model with HuggingFace Causal ML"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"7a355394-5b22-4c09-8d4f-9467a2fcfce4"},{"cell_type":"markdown","source":["![HuggingFace Logo](https://huggingface.co/front/assets/huggingface_logo-noborder.svg)\n","\n","**HuggingFace** is a popular open-source platform that develops computation tools for building application using machine learning. It is widely known for its Transformers library which contains open-source implementation of transformer models for text, image, and audio task.\n","\n","[**Phi 3**](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/) is a family of AI models developed by Microsoft, designed to redefine what is possible with small language models (SLMs). Phi-3 models are the most compatable and cost-effective SLMs, [outperforming models of the same size and even larger ones in language](https://news.microsoft.com/source/features/ai/the-phi-3-small-language-models-with-big-potential/?msockid=26355e446adb6dfa06484f956b686c27), reasoning, coding, and math benchmarks. \n","\n","<img src=\"https://pub-66c8c8c5ae474e9a9161c92b21de2f08.r2.dev/2024/04/The-Phi-3-small-language-models-with-big-potential-1.jpg\" alt=\"Phi 3 model performance\" width=\"600\">\n","\n","To make it easier to scale up causal language model prediction on a large dataset, we have integrated [HuggingFace Causal LM](https://huggingface.co/docs/transformers/tasks/language_modeling) with SynapseML. This integration makes it easy to use the Apache Spark distributed computing framework to process large data on text generation tasks.\n","\n","This tutorial shows hot to apply [phi3 model](https://huggingface.co/collections/microsoft/phi-3-6626e15e9585a200d2d761e3) at scale with no extra setting.\n"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"aa35ae52-6a9e-458d-91ee-ae3962ab5b68"},{"cell_type":"code","source":["chats = [\n","    (1, \"fix grammar: helol mi friend\"),\n","    (2, \"What is SynapseML\"),\n","    (3, \"translate to Spanish: hello\"),\n","]\n","\n","chat_df = spark.createDataFrame(chats, [\"row_index\", \"content\"])\n","chat_df.show()"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":9,"statement_ids":[9],"state":"finished","livy_statement_state":"available","session_id":"0c9f61cd-1288-4e0e-9c81-e054702855b3","normalized_state":"finished","queued_time":"2025-01-16T17:14:07.1864063Z","session_start_time":null,"execution_start_time":"2025-01-16T17:18:56.122231Z","execution_finish_time":"2025-01-16T17:19:03.4236677Z","parent_msg_id":"11078688-e7a5-4a37-8e95-6485d95aa809"},"text/plain":"StatementMeta(, 0c9f61cd-1288-4e0e-9c81-e054702855b3, 9, Finished, Available, Finished)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["+---------+--------------------+\n|row_index|             content|\n+---------+--------------------+\n|        1|fix grammar: helo...|\n|        2|   What is SynapseML|\n|        3|translate to Span...|\n+---------+--------------------+\n\n"]}],"execution_count":3,"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"7e76b540-466f-4ab3-9aa9-da8de5517fc1"},{"cell_type":"markdown","source":["## Define and Apply Phi3 model"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"ac0687e7-6609-4af4-a1a4-c098cb404374"},{"cell_type":"code","source":["from synapse.ml.llm.HuggingFaceCausallmTransform import HuggingFaceCausalLM\n","\n","phi3_transformer = (\n","    HuggingFaceCausalLM()\n","    .setModelName(\"microsoft/Phi-3-mini-4k-instruct\")\n","    .setInputCol(\"content\")\n","    .setOutputCol(\"result\")\n","    .setModelParam(max_new_tokens=1000)\n","    .setModelConfig(local_files_only=False, trust_remote_code=True)\n",")\n","result_df = phi3_transformer.transform(chat_df).collect()\n","display(result_df)"],"outputs":[],"execution_count":null,"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"},"collapsed":false,"jupyter":{"outputs_hidden":true},"editable":true,"run_control":{"frozen":false}},"id":"f8db55d9-b89d-420f-80e9-618041def698"},{"cell_type":"markdown","source":["## Use local cache\n","\n","By caching the model, you can reduce initialization time. On Fabric, store the model in a Lakehouse and use setCachePath to load it."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"4c839ac6-f92e-4615-a0c3-977a96231cc6"},{"cell_type":"code","source":["# %%sh\n","# azcopy copy \"https://mmlspark.blob.core.windows.net/huggingface/microsoft/Phi-3-mini-4k-instruct\" \"/lakehouse/default/Files/microsoft/\" --recursive=true"],"outputs":[],"execution_count":null,"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"9bc5edf1-35cb-45d6-b1dc-49a22a01484b"},{"cell_type":"code","source":["# phi3_transformer = (\n","#     HuggingFaceCausalLM()\n","#     .setCachePath(\"/lakehouse/default/Files/microsoft/Phi-3-mini-4k-instruct\")\n","#     .setInputCol(\"content\")\n","#     .setOutputCol(\"result\")\n","#     .setModelParam(max_new_tokens=1000)\n","# )\n","# result_df = phi3_transformer.transform(chat_df).collect()\n","# display(result_df)"],"outputs":[],"execution_count":null,"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"ee52c891-3be2-48fe-87b3-648e299a794e"}],"metadata":{"kernel_info":{"name":"synapse_pyspark"},"kernelspec":{"name":"synapse_pyspark","language":"Python","display_name":"Synapse PySpark"},"language_info":{"name":"python"},"microsoft":{"language":"python","language_group":"synapse_pyspark","ms_spell_check":{"ms_spell_check_language":"en"}},"nteract":{"version":"nteract-front-end@1.0.0"},"spark_compute":{"compute_id":"/trident/default","session_options":{"conf":{"spark.synapse.nbs.session.timeout":"1200000"}}},"synapse_widget":{"version":"0.1","state":{}},"dependencies":{"lakehouse":{"default_lakehouse":"cf3f397e-6a87-43ab-b8e0-bb9342e11c7a","default_lakehouse_name":"jessiwang_phi3","default_lakehouse_workspace_id":"4751a5bb-6a44-4164-8b31-c3b6a4cf1f8d"},"environment":{}}},"nbformat":4,"nbformat_minor":5}


before checkin this style needs to be fixed with black . in the top level dir

JessicaXYWang · 2025-01-17T21:52:48Z

/azp run

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JessicaXYWang · 2025-01-22T23:06:47Z

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JessicaXYWang · 2025-01-23T12:18:41Z

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JessicaXYWang · 2025-01-23T16:42:51Z

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JessicaXYWang added 2 commits September 12, 2024 09:28

poc

f0c2b00

poc

603777a

JessicaXYWang requested a review from mhamilton723 as a code owner October 15, 2024 06:12

Merge branch 'master' into phi3poc

47ae241

JessicaXYWang added 2 commits October 15, 2024 08:02

rename module

23f8ca0

Merge branch 'phi3poc' of https://github.com/JessicaXYWang/SynapseML …

bb5b2b6

…into phi3poc

JessicaXYWang added 2 commits October 17, 2024 15:03

update dependency

f235535

Merge branch 'master' into phi3poc

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add set device type

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JessicaXYWang added 3 commits January 2, 2025 08:11

add Downloader

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remove import

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Merge branch 'master' into phi3poc

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		@@ -0,0 +1 @@
		{"cells":[{"cell_type":"markdown","source":["# Apply Phi3 model with HuggingFace Causal ML"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"7a355394-5b22-4c09-8d4f-9467a2fcfce4"},{"cell_type":"markdown","source":["![HuggingFace Logo](https://huggingface.co/front/assets/huggingface_logo-noborder.svg)\n","\n","HuggingFace is a popular open-source platform that develops computation tools for building application using machine learning. It is widely known for its Transformers library which contains open-source implementation of transformer models for text, image, and audio task.\n","\n","[Phi 3](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/) is a family of AI models developed by Microsoft, designed to redefine what is possible with small language models (SLMs). Phi-3 models are the most compatable and cost-effective SLMs, [outperforming models of the same size and even larger ones in language](https://news.microsoft.com/source/features/ai/the-phi-3-small-language-models-with-big-potential/?msockid=26355e446adb6dfa06484f956b686c27), reasoning, coding, and math benchmarks. \n","\n","<img src=\"https://pub-66c8c8c5ae474e9a9161c92b21de2f08.r2.dev/2024/04/The-Phi-3-small-language-models-with-big-potential-1.jpg\" alt=\"Phi 3 model performance\" width=\"600\">\n","\n","To make it easier to scale up causal language model prediction on a large dataset, we have integrated [HuggingFace Causal LM](https://huggingface.co/docs/transformers/tasks/language_modeling) with SynapseML. This integration makes it easy to use the Apache Spark distributed computing framework to process large data on text generation tasks.\n","\n","This tutorial shows hot to apply [phi3 model](https://huggingface.co/collections/microsoft/phi-3-6626e15e9585a200d2d761e3) at scale with no extra setting.\n"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"aa35ae52-6a9e-458d-91ee-ae3962ab5b68"},{"cell_type":"code","source":["chats = [\n"," (1, \"fix grammar: helol mi friend\"),\n"," (2, \"What is SynapseML\"),\n"," (3, \"translate to Spanish: hello\"),\n","]\n","\n","chat_df = spark.createDataFrame(chats, [\"row_index\", \"content\"])\n","chat_df.show()"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":9,"statement_ids":[9],"state":"finished","livy_statement_state":"available","session_id":"0c9f61cd-1288-4e0e-9c81-e054702855b3","normalized_state":"finished","queued_time":"2025-01-16T17:14:07.1864063Z","session_start_time":null,"execution_start_time":"2025-01-16T17:18:56.122231Z","execution_finish_time":"2025-01-16T17:19:03.4236677Z","parent_msg_id":"11078688-e7a5-4a37-8e95-6485d95aa809"},"text/plain":"StatementMeta(, 0c9f61cd-1288-4e0e-9c81-e054702855b3, 9, Finished, Available, Finished)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["+---------+--------------------+\n\|row_index\| content\|\n+---------+--------------------+\n\| 1\|fix grammar: helo...\|\n\| 2\| What is SynapseML\|\n\| 3\|translate to Span...\|\n+---------+--------------------+\n\n"]}],"execution_count":3,"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"7e76b540-466f-4ab3-9aa9-da8de5517fc1"},{"cell_type":"markdown","source":["## Define and Apply Phi3 model"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"ac0687e7-6609-4af4-a1a4-c098cb404374"},{"cell_type":"code","source":["from synapse.ml.llm.HuggingFaceCausallmTransform import HuggingFaceCausalLM\n","\n","phi3_transformer = (\n"," HuggingFaceCausalLM()\n"," .setModelName(\"microsoft/Phi-3-mini-4k-instruct\")\n"," .setInputCol(\"content\")\n"," .setOutputCol(\"result\")\n"," .setModelParam(max_new_tokens=1000)\n"," .setModelConfig(local_files_only=False, trust_remote_code=True)\n",")\n","result_df = phi3_transformer.transform(chat_df).collect()\n","display(result_df)"],"outputs":[],"execution_count":null,"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"},"collapsed":false,"jupyter":{"outputs_hidden":true},"editable":true,"run_control":{"frozen":false}},"id":"f8db55d9-b89d-420f-80e9-618041def698"},{"cell_type":"markdown","source":["## Use local cache\n","\n","By caching the model, you can reduce initialization time. On Fabric, store the model in a Lakehouse and use setCachePath to load it."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"4c839ac6-f92e-4615-a0c3-977a96231cc6"},{"cell_type":"code","source":["# %%sh\n","# azcopy copy \"https://mmlspark.blob.core.windows.net/huggingface/microsoft/Phi-3-mini-4k-instruct\" \"/lakehouse/default/Files/microsoft/\" --recursive=true"],"outputs":[],"execution_count":null,"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"9bc5edf1-35cb-45d6-b1dc-49a22a01484b"},{"cell_type":"code","source":["# phi3_transformer = (\n","# HuggingFaceCausalLM()\n","# .setCachePath(\"/lakehouse/default/Files/microsoft/Phi-3-mini-4k-instruct\")\n","# .setInputCol(\"content\")\n","# .setOutputCol(\"result\")\n","# .setModelParam(max_new_tokens=1000)\n","# )\n","# result_df = phi3_transformer.transform(chat_df).collect()\n","# display(result_df)"],"outputs":[],"execution_count":null,"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"ee52c891-3be2-48fe-87b3-648e299a794e"}],"metadata":{"kernel_info":{"name":"synapse_pyspark"},"kernelspec":{"name":"synapse_pyspark","language":"Python","display_name":"Synapse PySpark"},"language_info":{"name":"python"},"microsoft":{"language":"python","language_group":"synapse_pyspark","ms_spell_check":{"ms_spell_check_language":"en"}},"nteract":{"version":"nteract-front-end@1.0.0"},"spark_compute":{"compute_id":"/trident/default","session_options":{"conf":{"spark.synapse.nbs.session.timeout":"1200000"}}},"synapse_widget":{"version":"0.1","state":{}},"dependencies":{"lakehouse":{"default_lakehouse":"cf3f397e-6a87-43ab-b8e0-bb9342e11c7a","default_lakehouse_name":"jessiwang_phi3","default_lakehouse_workspace_id":"4751a5bb-6a44-4164-8b31-c3b6a4cf1f8d"},"environment":{}}},"nbformat":4,"nbformat_minor":5}

[WIP] Phi3poc #2301

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JessicaXYWang commented Oct 15, 2024

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What changes are proposed in this pull request?

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Does this PR add a new feature? If so, have you added samples on website?

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