From 4e49666577c195ecf2260dee31afe622d4020bfe Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 12 Dec 2023 17:54:26 +0000 Subject: [PATCH 01/49] Added Japanese dependencies to mlserver-huggingface --- runtimes/huggingface/poetry.lock | 108 +++++++++++++++++++++++++++- runtimes/huggingface/pyproject.toml | 4 ++ 2 files changed, 110 insertions(+), 2 deletions(-) diff --git a/runtimes/huggingface/poetry.lock b/runtimes/huggingface/poetry.lock index d36b5e058..6f393ff6c 100644 --- a/runtimes/huggingface/poetry.lock +++ b/runtimes/huggingface/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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"tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"}, ] +[[package]] +name = "unidic" +version = "1.1.0" +description = "UniDic packaged for Python" +optional = false +python-versions = ">=3.5" +files = [ + {file = "unidic-1.1.0.tar.gz", hash = "sha256:0ab91c05de342c84d2a6314901fd3afb9061ecd7534dd4a0431dccbb87d921b7"}, +] + +[package.dependencies] +plac = ">=1.1.3,<2.0.0" +requests = ">=2.22.0,<3.0.0" +tqdm = ">=4.41.1,<5.0.0" +wasabi = ">=0.6.0,<1.0.0" + +[[package]] +name = "unidic-lite" +version = "1.0.8" +description = "A small version of UniDic packaged for Python" +optional = false +python-versions = "*" +files = [ + {file = "unidic-lite-1.0.8.tar.gz", hash = "sha256:db9d4572d9fdd4d00a97949d4b0741ec480ee05a7e7e2e32f547500dae27b245"}, +] + [[package]] name = "urllib3" version = "2.0.7" @@ -3923,6 +4016,17 @@ dev = ["Cython (>=0.29.32,<0.30.0)", "Sphinx (>=4.1.2,<4.2.0)", "aiohttp", "flak docs = ["Sphinx (>=4.1.2,<4.2.0)", "sphinx-rtd-theme (>=0.5.2,<0.6.0)", "sphinxcontrib-asyncio (>=0.3.0,<0.4.0)"] test = ["Cython (>=0.29.32,<0.30.0)", "aiohttp", "flake8 (>=3.9.2,<3.10.0)", "mypy (>=0.800)", "psutil", "pyOpenSSL (>=22.0.0,<22.1.0)", "pycodestyle (>=2.7.0,<2.8.0)"] +[[package]] +name = "wasabi" +version = "0.10.1" +description = "A lightweight console printing and formatting toolkit" +optional = false +python-versions = "*" +files = [ + {file = "wasabi-0.10.1-py3-none-any.whl", hash = "sha256:fe862cc24034fbc9f04717cd312ab884f71f51a8ecabebc3449b751c2a649d83"}, + {file = "wasabi-0.10.1.tar.gz", hash = "sha256:c8e372781be19272942382b14d99314d175518d7822057cb7a97010c4259d249"}, +] + [[package]] name = "werkzeug" version = "3.0.1" @@ -4301,4 +4405,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" python-versions = "^3.8.1,<3.12" -content-hash = "f1b2a658fab2441fe31b04b2bc9851075b59b2c4f4d357d24528b4920a55d726" +content-hash = "0d522cec68c1a16b88f4188e9609f11373c04653725dc38e797ab8ac3c79f275" diff --git a/runtimes/huggingface/pyproject.toml b/runtimes/huggingface/pyproject.toml index 3c0677e3e..9c4ff90b2 100644 --- a/runtimes/huggingface/pyproject.toml +++ b/runtimes/huggingface/pyproject.toml @@ -13,6 +13,10 @@ mlserver = "*" tensorflow = "*" pillow = "*" optimum = {extras = ["onnxruntime"], version = ">=1.4,<2.0"} +fugashi = "^1.3.0" +unidic = "^1.1.0" +unidic-lite = "^1.0.8" +ipadic = "^1.0.0" [tool.poetry.group.dev.dependencies] mlserver = {path = "../..", develop = true} From e6562e1e80e5957c07fb019412aafd1ca9cc4362 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 12 Dec 2023 19:25:25 +0000 Subject: [PATCH 02/49] Added unit test for Japanese server --- .../test_tasks/test_japanese_mask_fill.py | 47 +++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py new file mode 100644 index 000000000..582f9f270 --- /dev/null +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -0,0 +1,47 @@ +import pytest +from mlserver.types import InferenceRequest, RequestInput, Parameters +from mlserver.settings import ModelSettings, ModelParameters +from mlserver_huggingface import HuggingFaceRuntime +from mlserver_huggingface.codecs import HuggingfaceRequestCodec + + +test_sentence = "実際に空が[MASK]のか？" + +@pytest.fixture +def current_model_settings(): + # if not request.config.option.test_hg_tasks: + # pytest.skip() + return ModelSettings( + name="model", + implementation=HuggingFaceRuntime, + parameters=ModelParameters( + extra={ + "task": "fill-mask", + "pretrained_model": "cl-tohoku/bert-base-japanese", + "pretrained_tokenizer": "cl-tohoku/bert-base-japanese", + } + ), + ) + +@pytest.fixture +async def current_runtime(current_model_settings: ModelSettings) -> HuggingFaceRuntime: + runtime = HuggingFaceRuntime(current_model_settings) + await runtime.load() + return runtime + +@pytest.fixture +def japanese_text_request(): + inputs = [ + test_sentence + ] + req = HuggingfaceRequestCodec.encode_request( + {"inputs" : inputs}, + use_bytes=False, + ) + return req + +async def test_infer(current_runtime, japanese_text_request): + resp = await current_runtime.predict(japanese_text_request) + decoded = HuggingfaceRequestCodec.decode_response(resp) + top_result = list(decoded.items())[0][1][0] + assert top_result['token_str'] == "見える", "Inference is incorrect!" \ No newline at end of file From 4414ccff0cec7785b8d77ac70b7756609c504d56 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 12 Dec 2023 19:35:10 +0000 Subject: [PATCH 03/49] linted --- .../test_tasks/test_japanese_mask_fill.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py index 582f9f270..bdf3c8cfc 100644 --- a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -1,5 +1,4 @@ import pytest -from mlserver.types import InferenceRequest, RequestInput, Parameters from mlserver.settings import ModelSettings, ModelParameters from mlserver_huggingface import HuggingFaceRuntime from mlserver_huggingface.codecs import HuggingfaceRequestCodec @@ -7,10 +6,11 @@ test_sentence = "実際に空が[MASK]のか？" + @pytest.fixture -def current_model_settings(): - # if not request.config.option.test_hg_tasks: - # pytest.skip() +def current_model_settings(request) -> ModelSettings: + if not request.config.option.test_hg_tasks: + pytest.skip() return ModelSettings( name="model", implementation=HuggingFaceRuntime, @@ -23,25 +23,26 @@ def current_model_settings(): ), ) + @pytest.fixture async def current_runtime(current_model_settings: ModelSettings) -> HuggingFaceRuntime: runtime = HuggingFaceRuntime(current_model_settings) await runtime.load() return runtime + @pytest.fixture def japanese_text_request(): - inputs = [ - test_sentence - ] + inputs = [test_sentence] req = HuggingfaceRequestCodec.encode_request( - {"inputs" : inputs}, + {"inputs": inputs}, use_bytes=False, ) return req + async def test_infer(current_runtime, japanese_text_request): resp = await current_runtime.predict(japanese_text_request) decoded = HuggingfaceRequestCodec.decode_response(resp) top_result = list(decoded.items())[0][1][0] - assert top_result['token_str'] == "見える", "Inference is incorrect!" \ No newline at end of file + assert top_result["token_str"] == "見える", "Inference is incorrect!" From 399d83be4a8d2dade9a249dceb5816eb8a44d504 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Thu, 14 Dec 2023 00:35:38 +0000 Subject: [PATCH 04/49] Addressed style comments; made dependencies optional --- runtimes/huggingface/poetry.lock | 3199 ++++++++--------- runtimes/huggingface/pyproject.toml | 11 +- runtimes/huggingface/tests/conftest.py | 1 + .../test_tasks/test_japanese_mask_fill.py | 21 +- 4 files changed, 1607 insertions(+), 1625 deletions(-) diff --git a/runtimes/huggingface/poetry.lock b/runtimes/huggingface/poetry.lock index 6f393ff6c..fe8161d9e 100644 --- a/runtimes/huggingface/poetry.lock +++ b/runtimes/huggingface/poetry.lock @@ -2,13 +2,13 @@ [[package]] name = "absl-py" -version = "1.4.0" +version = "2.0.0" description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" files = [ - {file = "absl-py-1.4.0.tar.gz", hash = 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= "0d522cec68c1a16b88f4188e9609f11373c04653725dc38e797ab8ac3c79f275" +content-hash = "4fbb2c4c7ceac3faefd044eaded0154f2818a2d78d3679d5b34fc3e4c3281741" diff --git a/runtimes/huggingface/pyproject.toml b/runtimes/huggingface/pyproject.toml index 9c4ff90b2..37e4e8edb 100644 --- a/runtimes/huggingface/pyproject.toml +++ b/runtimes/huggingface/pyproject.toml @@ -13,10 +13,10 @@ mlserver = "*" tensorflow = "*" pillow = "*" optimum = {extras = ["onnxruntime"], version = ">=1.4,<2.0"} -fugashi = "^1.3.0" -unidic = "^1.1.0" -unidic-lite = "^1.0.8" -ipadic = "^1.0.0" +fugashi = {version = "^1.3.0", optional = true} +unidic = {version = "^1.1.0", optional = true} +unidic-lite = {version = "^1.0.8", optional = true} +ipadic = {version = "^1.0.0", optional = true} [tool.poetry.group.dev.dependencies] mlserver = {path = "../..", develop = true} @@ -24,6 +24,9 @@ torch = "2.0.0" transformers = ">=4.30,<4.36" tensorflow = ">=2.12,<2.14" +[tool.poetry.extras] +japanese = ["fugashi", "unidic", "unidic-lite", "ipadic"] + [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/runtimes/huggingface/tests/conftest.py b/runtimes/huggingface/tests/conftest.py index 7b6c07998..02744eef2 100644 --- a/runtimes/huggingface/tests/conftest.py +++ b/runtimes/huggingface/tests/conftest.py @@ -9,6 +9,7 @@ # if not provide this command argument, task tests skiped def pytest_addoption(parser): parser.addoption("--test-hg-tasks", action="store_true", default=False) + parser.addoption("--test-ja-support", action="store_true", default=False) @pytest.fixture() diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py index bdf3c8cfc..c37ab3b80 100644 --- a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -4,13 +4,13 @@ from mlserver_huggingface.codecs import HuggingfaceRequestCodec -test_sentence = "実際に空が[MASK]のか？" - - @pytest.fixture -def current_model_settings(request) -> ModelSettings: - if not request.config.option.test_hg_tasks: +def bert_japanese_settings(request) -> ModelSettings: + if (not request.config.option.test_hg_tasks) or ( + not request.config.option.test_ja_support + ): pytest.skip() + return ModelSettings( name="model", implementation=HuggingFaceRuntime, @@ -25,14 +25,17 @@ def current_model_settings(request) -> ModelSettings: @pytest.fixture -async def current_runtime(current_model_settings: ModelSettings) -> HuggingFaceRuntime: - runtime = HuggingFaceRuntime(current_model_settings) +async def current_runtime(bert_japanese_settings: ModelSettings) -> HuggingFaceRuntime: + runtime = HuggingFaceRuntime(bert_japanese_settings) await runtime.load() return runtime @pytest.fixture def japanese_text_request(): + # Test sentence: Is the sky really [MASK]? + test_sentence = "実際に空が[MASK]のか？" + inputs = [test_sentence] req = HuggingfaceRequestCodec.encode_request( {"inputs": inputs}, @@ -42,7 +45,9 @@ def japanese_text_request(): async def test_infer(current_runtime, japanese_text_request): + # [MASK] = visible + expected_output = "見える" resp = await current_runtime.predict(japanese_text_request) decoded = HuggingfaceRequestCodec.decode_response(resp) top_result = list(decoded.items())[0][1][0] - assert top_result["token_str"] == "見える", "Inference is incorrect!" + assert top_result["token_str"] == expected_output, "Inference is correct!" From 854784f6b1f7b8f32cb8c00f13f47181bb9cf68b Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 22:57:55 +0000 Subject: [PATCH 05/49] Updated README.md and unit test --- docs/examples/huggingface/README.md | 34 +++++++++++++++++++ .../test_tasks/test_japanese_mask_fill.py | 9 ++--- 2 files changed, 39 insertions(+), 4 deletions(-) diff --git a/docs/examples/huggingface/README.md b/docs/examples/huggingface/README.md index 9db1f14b3..405ed1a3a 100644 --- a/docs/examples/huggingface/README.md +++ b/docs/examples/huggingface/README.md @@ -199,6 +199,40 @@ inference_request = { requests.post("http://localhost:8080/v2/models/transformer/infer", json=inference_request).json() ``` +### Masked Language Modeling + +We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers. + +```python +%%writefile ./model-settings.json +{ + "name": "model", + "implementation": "mlserver_huggingface.runtime.HuggingFaceRuntime", + "parameters": { + "extra": { + "task": "fill-mask", + "pretrained_model": "cl-tohoku/bert-base-japanese", + "pretrained_tokenizer": "cl-tohoku/bert-base-japanese" + } + } +} +``` +Using the shell to start mlserver like so, +```shell +mlserver start . +``` +we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens. +```python +# Test sentence: Is the sky really [MASK]? +test_sentence = "実際に空が[MASK]のか？" +# [MASK] = visible +expected_output = "見える" +inference_request = { + "inputs": [ + test_sentence + ] +} +``` ## GPU Acceleration We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py index c37ab3b80..4eeaf3c5d 100644 --- a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -35,19 +35,20 @@ async def current_runtime(bert_japanese_settings: ModelSettings) -> HuggingFaceR def japanese_text_request(): # Test sentence: Is the sky really [MASK]? test_sentence = "実際に空が[MASK]のか？" + # [MASK] = visible + expected_output = "見える" inputs = [test_sentence] req = HuggingfaceRequestCodec.encode_request( {"inputs": inputs}, use_bytes=False, ) - return req + return req, expected_output async def test_infer(current_runtime, japanese_text_request): - # [MASK] = visible - expected_output = "見える" - resp = await current_runtime.predict(japanese_text_request) + req, expected_output = japanese_text_request + resp = await current_runtime.predict(req) decoded = HuggingfaceRequestCodec.decode_response(resp) top_result = list(decoded.items())[0][1][0] assert top_result["token_str"] == expected_output, "Inference is correct!" From c6f1a4f57544ccaa54ed52a409316e6acff9d02e Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 15:22:30 -0800 Subject: [PATCH 06/49] Updated docs --- docs/examples/huggingface/README.ipynb | 294 +++++++++---------------- docs/examples/huggingface/README.md | 13 +- 2 files changed, 112 insertions(+), 195 deletions(-) diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index d6bae76d4..e032dd56d 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -18,11 +18,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "b5b2588c", "metadata": {}, "outputs": [], "source": [ + "!pip install requests\n", "# Import required dependencies\n", "import requests" ] @@ -42,18 +43,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "6df62443", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -92,28 +85,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "759ad7df", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': 'eb160c6b-8223-4342-ad92-6ac301a9fa5d',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"generated_text\": \"this is a testnet with 1-3,000-bit nodes as nodes.\"}']}]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -143,18 +118,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "6d185281", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -194,28 +161,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "39d8b438", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': '9c482c8d-b21e-44b1-8a42-7650a9dc01ef',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"generated_text\": \"this is a test of the \\\\\"safe-code-safe-code-safe-code\\\\\" approach. The method only accepts two parameters as parameters: the code. The parameter \\'unsafe-code-safe-code-safe-code\\' should\"}']}]}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -252,18 +201,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "4492dc01", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -291,30 +232,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "d7aaf365", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': '4efac938-86d8-41a1-b78f-7690b2dcf197',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"score\": 0.9869915843009949, \"start\": 12, \"end\": 18, \"answer\": \"Seldon\"}']}]}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -346,18 +269,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "8e70c7d7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -385,30 +300,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "2f704413", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"label\": \"NEGATIVE\", \"score\": 0.9996137022972107}']}]}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -424,6 +321,73 @@ "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "545307aa", + "metadata": {}, + "source": [ + "### Masked Language Modeling\n", + "\n", + "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86ed27dc", + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"model\",\n", + " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"fill-mask\",\n", + " \"pretrained_model\": \"cl-tohoku/bert-base-japanese\",\n", + " \"pretrained_tokenizer\": \"cl-tohoku/bert-base-japanese\"\n", + " }\n", + " }\n", + "}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f4d34159", + "metadata": {}, + "source": [ + "Using the shell to start mlserver like so,\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d981304d", + "metadata": {}, + "outputs": [], + "source": [ + "from mlserver_huggingface import HuggingFaceRuntime\n", + "# Test sentence: Is the sky really [MASK]?\n", + "test_sentence = \"実際に空が[MASK]のか？\"\n", + "# [MASK] = visible\n", + "expected_output = \"見える\"\n", + "\n", + "\n", + "inference_request = HuggingfaceRequestCodec.encode_request(\n", + " {\"inputs\": [test_sentence]},\n", + " use_bytes=False,\n", + ")\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" + ] + }, { "cell_type": "markdown", "id": "fe6655d9", @@ -440,18 +404,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "827472eb", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -482,18 +438,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "888501c1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time: 66.42268538899953\n" - ] - } - ], + "outputs": [], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -537,18 +485,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "032b8f4e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -565,18 +505,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "1b6bd339", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time: 11.27933280000434\n" - ] - } - ], + "outputs": [], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -622,18 +554,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "810a4abe", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -663,25 +587,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "1a787909", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requests [total, rate, throughput] 150, 50.34, 22.28\n", - "Duration [total, attack, wait] 6.732s, 2.98s, 3.753s\n", - "Latencies [min, mean, 50, 90, 95, 99, max] 1.975s, 3.168s, 3.22s, 4.065s, 4.183s, 4.299s, 4.318s\n", - "Bytes In [total, mean] 60978, 406.52\n", - "Bytes Out [total, mean] 12300, 82.00\n", - "Success [ratio] 100.00%\n", - "Status Codes [code:count] 200:150 \n", - "Error Set:\n" - ] - } - ], + "outputs": [], "source": [ "%%bash\n", "jq -ncM '{\"method\": \"POST\", \"header\": {\"Content-Type\": [\"application/json\"] }, \"url\": \"http://localhost:8080/v2/models/transformer/infer\", \"body\": \"{\\\"inputs\\\":[{\\\"name\\\":\\\"text_inputs\\\",\\\"shape\\\":[1],\\\"datatype\\\":\\\"BYTES\\\",\\\"data\\\":[\\\"test\\\"]}]}\" | @base64 }' \\\n", @@ -707,7 +616,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -721,7 +630,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.8" + "version": "3.9.7" + }, + "vscode": { + "interpreter": { + "hash": "dc5eb0c90e2f6c976a983a2fa1f7afdeb9926592d32c540a8be7fcdb78a03d65" + } } }, "nbformat": 4, diff --git a/docs/examples/huggingface/README.md b/docs/examples/huggingface/README.md index 405ed1a3a..c16116e2a 100644 --- a/docs/examples/huggingface/README.md +++ b/docs/examples/huggingface/README.md @@ -223,15 +223,18 @@ mlserver start . ``` we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens. ```python +from mlserver_huggingface import HuggingFaceRuntime # Test sentence: Is the sky really [MASK]? test_sentence = "実際に空が[MASK]のか？" # [MASK] = visible expected_output = "見える" -inference_request = { - "inputs": [ - test_sentence - ] -} + + +inference_request = HuggingfaceRequestCodec.encode_request( + {"inputs": [test_sentence]}, + use_bytes=False, +) +requests.post("http://localhost:8080/v2/models/transformer/infer", json=inference_request).json() ``` ## GPU Acceleration From f75ee9eba8db41e90e3cbf35c0ed9ae12cba3cdd Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 15:54:11 -0800 Subject: [PATCH 07/49] Working test in docs --- docs/examples/huggingface/README.ipynb | 47 ++++++++++++++----- docs/examples/huggingface/README.md | 9 ++-- docs/examples/huggingface/model-settings.json | 11 +++++ 3 files changed, 51 insertions(+), 16 deletions(-) create mode 100644 docs/examples/huggingface/model-settings.json diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index e032dd56d..cc6e73e2c 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -18,12 +18,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "b5b2588c", "metadata": {}, "outputs": [], "source": [ - "!pip install requests\n", "# Import required dependencies\n", "import requests" ] @@ -334,14 +333,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "86ed27dc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", - " \"name\": \"model\",\n", + " \"name\": \"transformer\",\n", " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", " \"parameters\": {\n", " \"extra\": {\n", @@ -369,12 +376,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "d981304d", "metadata": {}, - "outputs": [], - "source": [ - "from mlserver_huggingface import HuggingFaceRuntime\n", + "outputs": [ + { + "data": { + "text/plain": [ + "'実際 に 空 が 見える の か?'" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlserver_huggingface.codecs import HuggingfaceRequestCodec\n", + "import json\n", "# Test sentence: Is the sky really [MASK]?\n", "test_sentence = \"実際に空が[MASK]のか？\"\n", "# [MASK] = visible\n", @@ -385,7 +404,9 @@ " {\"inputs\": [test_sentence]},\n", " use_bytes=False,\n", ")\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" + "json.dumps(inference_request.dict())\n", + "response = requests.post(\"http://localhost:8080/v2/models/model/infer\", json=inference_request.dict()).json()\n", + "json.loads(response['outputs'][0]['data'][0])[\"sequence\"]" ] }, { @@ -616,9 +637,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "mlserver_env", "language": "python", - "name": "python3" + "name": "mlserver_env" }, "language_info": { "codemirror_mode": { @@ -630,7 +651,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.5" }, "vscode": { "interpreter": { diff --git a/docs/examples/huggingface/README.md b/docs/examples/huggingface/README.md index c16116e2a..993ee6def 100644 --- a/docs/examples/huggingface/README.md +++ b/docs/examples/huggingface/README.md @@ -223,18 +223,21 @@ mlserver start . ``` we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens. ```python -from mlserver_huggingface import HuggingFaceRuntime +from mlserver_huggingface.codecs import HuggingfaceRequestCodec +import json + # Test sentence: Is the sky really [MASK]? test_sentence = "実際に空が[MASK]のか？" # [MASK] = visible expected_output = "見える" - inference_request = HuggingfaceRequestCodec.encode_request( {"inputs": [test_sentence]}, use_bytes=False, ) -requests.post("http://localhost:8080/v2/models/transformer/infer", json=inference_request).json() +json.dumps(inference_request.dict()) +response = requests.post("http://localhost:8080/v2/models/transformer/infer", json=inference_request.dict()).json() +json.loads(response['outputs'][0]['data'][0])["sequence"] ``` ## GPU Acceleration diff --git a/docs/examples/huggingface/model-settings.json b/docs/examples/huggingface/model-settings.json new file mode 100644 index 000000000..88b2d714c --- /dev/null +++ b/docs/examples/huggingface/model-settings.json @@ -0,0 +1,11 @@ +{ + "name": "transformer", + "implementation": "mlserver_huggingface.runtime.HuggingFaceRuntime", + "parameters": { + "extra": { + "task": "fill-mask", + "pretrained_model": "cl-tohoku/bert-base-japanese", + "pretrained_tokenizer": "cl-tohoku/bert-base-japanese" + } + } +} From 936fadf99323fd2957c34568a7b45ff79659f065 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 16:09:52 -0800 Subject: [PATCH 08/49] Generalized test to a list of cases --- .../test_tasks/test_japanese_mask_fill.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py index 4eeaf3c5d..8e97e60a2 100644 --- a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -38,17 +38,19 @@ def japanese_text_request(): # [MASK] = visible expected_output = "見える" - inputs = [test_sentence] - req = HuggingfaceRequestCodec.encode_request( - {"inputs": inputs}, + outputs = [expected_output] + reqs = HuggingfaceRequestCodec.encode_request( + {"inputs": [test_sentence]}, use_bytes=False, ) - return req, expected_output + return reqs, outputs async def test_infer(current_runtime, japanese_text_request): - req, expected_output = japanese_text_request - resp = await current_runtime.predict(req) + reqs, expected_outputs = japanese_text_request + resp = await current_runtime.predict(reqs) decoded = HuggingfaceRequestCodec.decode_response(resp) - top_result = list(decoded.items())[0][1][0] - assert top_result["token_str"] == expected_output, "Inference is correct!" + + for req, expected_output in zip(list(decoded.items()), expected_outputs): + top_result = req[1][0] + assert top_result["token_str"] == expected_output, "Inference is correct!" From 5b3a2587c01ba78d4b57b452514c97d58eb9bc91 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 16:11:12 -0800 Subject: [PATCH 09/49] updated docs --- docs/examples/huggingface/README.ipynb | 4 ++-- docs/examples/huggingface/model-settings.json | 11 ----------- 2 files changed, 2 insertions(+), 13 deletions(-) delete mode 100644 docs/examples/huggingface/model-settings.json diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index cc6e73e2c..dc56c3763 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -394,18 +394,18 @@ "source": [ "from mlserver_huggingface.codecs import HuggingfaceRequestCodec\n", "import json\n", + "\n", "# Test sentence: Is the sky really [MASK]?\n", "test_sentence = \"実際に空が[MASK]のか？\"\n", "# [MASK] = visible\n", "expected_output = \"見える\"\n", "\n", - "\n", "inference_request = HuggingfaceRequestCodec.encode_request(\n", " {\"inputs\": [test_sentence]},\n", " use_bytes=False,\n", ")\n", "json.dumps(inference_request.dict())\n", - "response = requests.post(\"http://localhost:8080/v2/models/model/infer\", json=inference_request.dict()).json()\n", + "response = requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request.dict()).json()\n", "json.loads(response['outputs'][0]['data'][0])[\"sequence\"]" ] }, diff --git a/docs/examples/huggingface/model-settings.json b/docs/examples/huggingface/model-settings.json deleted file mode 100644 index 88b2d714c..000000000 --- a/docs/examples/huggingface/model-settings.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "name": "transformer", - "implementation": "mlserver_huggingface.runtime.HuggingFaceRuntime", - "parameters": { - "extra": { - "task": "fill-mask", - "pretrained_model": "cl-tohoku/bert-base-japanese", - "pretrained_tokenizer": "cl-tohoku/bert-base-japanese" - } - } -} From c30f2ad64b8bfaf16d3ac5cbd43db10fefea38c4 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 16:47:52 -0800 Subject: [PATCH 10/49] Fixed notebook cell outputs --- docs/examples/huggingface/README.ipynb | 317 +++++++++++++++---------- 1 file changed, 191 insertions(+), 126 deletions(-) diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index dc56c3763..d6bae76d4 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "id": "b5b2588c", "metadata": {}, "outputs": [], @@ -42,10 +42,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "6df62443", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -84,10 +92,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "759ad7df", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': 'eb160c6b-8223-4342-ad92-6ac301a9fa5d',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"generated_text\": \"this is a testnet with 1-3,000-bit nodes as nodes.\"}']}]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -117,10 +143,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "6d185281", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -160,10 +194,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "39d8b438", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '9c482c8d-b21e-44b1-8a42-7650a9dc01ef',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"generated_text\": \"this is a test of the \\\\\"safe-code-safe-code-safe-code\\\\\" approach. The method only accepts two parameters as parameters: the code. The parameter \\'unsafe-code-safe-code-safe-code\\' should\"}']}]}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -200,10 +252,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "4492dc01", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -231,12 +291,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "d7aaf365", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '4efac938-86d8-41a1-b78f-7690b2dcf197',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"score\": 0.9869915843009949, \"start\": 12, \"end\": 18, \"answer\": \"Seldon\"}']}]}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -268,10 +346,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "8e70c7d7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -299,12 +385,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "2f704413", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"label\": \"NEGATIVE\", \"score\": 0.9996137022972107}']}]}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -320,95 +424,6 @@ "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "545307aa", - "metadata": {}, - "source": [ - "### Masked Language Modeling\n", - "\n", - "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "86ed27dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"fill-mask\",\n", - " \"pretrained_model\": \"cl-tohoku/bert-base-japanese\",\n", - " \"pretrained_tokenizer\": \"cl-tohoku/bert-base-japanese\"\n", - " }\n", - " }\n", - "}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f4d34159", - "metadata": {}, - "source": [ - "Using the shell to start mlserver like so,\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```\n", - "we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "d981304d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'実際 に 空 が 見える の か?'" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlserver_huggingface.codecs import HuggingfaceRequestCodec\n", - "import json\n", - "\n", - "# Test sentence: Is the sky really [MASK]?\n", - "test_sentence = \"実際に空が[MASK]のか？\"\n", - "# [MASK] = visible\n", - "expected_output = \"見える\"\n", - "\n", - "inference_request = HuggingfaceRequestCodec.encode_request(\n", - " {\"inputs\": [test_sentence]},\n", - " use_bytes=False,\n", - ")\n", - "json.dumps(inference_request.dict())\n", - "response = requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request.dict()).json()\n", - "json.loads(response['outputs'][0]['data'][0])[\"sequence\"]" - ] - }, { "cell_type": "markdown", "id": "fe6655d9", @@ -425,10 +440,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "827472eb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -459,10 +482,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "888501c1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time: 66.42268538899953\n" + ] + } + ], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -506,10 +537,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "032b8f4e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -526,10 +565,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "1b6bd339", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time: 11.27933280000434\n" + ] + } + ], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -575,10 +622,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "810a4abe", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -608,10 +663,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "1a787909", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requests [total, rate, throughput] 150, 50.34, 22.28\n", + "Duration [total, attack, wait] 6.732s, 2.98s, 3.753s\n", + "Latencies [min, mean, 50, 90, 95, 99, max] 1.975s, 3.168s, 3.22s, 4.065s, 4.183s, 4.299s, 4.318s\n", + "Bytes In [total, mean] 60978, 406.52\n", + "Bytes Out [total, mean] 12300, 82.00\n", + "Success [ratio] 100.00%\n", + "Status Codes [code:count] 200:150 \n", + "Error Set:\n" + ] + } + ], "source": [ "%%bash\n", "jq -ncM '{\"method\": \"POST\", \"header\": {\"Content-Type\": [\"application/json\"] }, \"url\": \"http://localhost:8080/v2/models/transformer/infer\", \"body\": \"{\\\"inputs\\\":[{\\\"name\\\":\\\"text_inputs\\\",\\\"shape\\\":[1],\\\"datatype\\\":\\\"BYTES\\\",\\\"data\\\":[\\\"test\\\"]}]}\" | @base64 }' \\\n", @@ -637,9 +707,9 @@ ], "metadata": { "kernelspec": { - "display_name": "mlserver_env", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "mlserver_env" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -651,12 +721,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.5" - }, - "vscode": { - "interpreter": { - "hash": "dc5eb0c90e2f6c976a983a2fa1f7afdeb9926592d32c540a8be7fcdb78a03d65" - } + "version": "3.9.8" } }, "nbformat": 4, From 339527fc730c7e5bb3c297b066513d0c4a2b8b1a Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 16:49:50 -0800 Subject: [PATCH 11/49] Fixed notebook cell outputs --- docs/examples/huggingface/README.ipynb | 88 +++++++++++++++++++++++++- 1 file changed, 87 insertions(+), 1 deletion(-) diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index d6bae76d4..b7e69b60b 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -424,6 +424,92 @@ "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" ] }, + { + "cell_type": "markdown", + "id": "f1d4b24a-4c09-4743-a086-6f8b143711ad", + "metadata": {}, + "source": [ + "### Masked Language Modeling\n", + "\n", + "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "63e368ee-a5ef-44b7-aab8-cafd30ab227a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"fill-mask\",\n", + " \"pretrained_model\": \"cl-tohoku/bert-base-japanese\",\n", + " \"pretrained_tokenizer\": \"cl-tohoku/bert-base-japanese\"\n", + " }\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "72b05244-f52f-41c0-a1bc-a5d11d1c75a0", + "metadata": {}, + "source": [ + "Using the shell to start mlserver like so,\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b619c852-f35d-4506-91e1-0a1e3bcb7b8b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'実際 に 空 が 見える の か?'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlserver_huggingface.codecs import HuggingfaceRequestCodec\n", + "import json\n", + "\n", + "# Test sentence: Is the sky really [MASK]?\n", + "test_sentence = \"実際に空が[MASK]のか？\"\n", + "# [MASK] = visible\n", + "expected_output = \"見える\"\n", + "\n", + "inference_request = HuggingfaceRequestCodec.encode_request(\n", + " {\"inputs\": [test_sentence]},\n", + " use_bytes=False,\n", + ")\n", + "json.dumps(inference_request.dict())\n", + "response = requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request.dict()).json()\n", + "json.loads(response['outputs'][0]['data'][0])[\"sequence\"]" + ] + }, { "cell_type": "markdown", "id": "fe6655d9", @@ -721,7 +807,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.8" + "version": "3.9.5" } }, "nbformat": 4, From f44656d6950f441effba019ccd97aad691deac11 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 26 Dec 2023 09:49:53 -0800 Subject: [PATCH 12/49] Refactored test with pytest.pymark.parameterize. Used a smaller test case. --- .../test_tasks/test_japanese_mask_fill.py | 82 +++++++++++-------- 1 file changed, 46 insertions(+), 36 deletions(-) diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py index 8e97e60a2..028c2c72d 100644 --- a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -4,53 +4,63 @@ from mlserver_huggingface.codecs import HuggingfaceRequestCodec -@pytest.fixture -def bert_japanese_settings(request) -> ModelSettings: - if (not request.config.option.test_hg_tasks) or ( - not request.config.option.test_ja_support - ): - pytest.skip() +""" +Test case summary: - return ModelSettings( - name="model", - implementation=HuggingFaceRuntime, - parameters=ModelParameters( - extra={ - "task": "fill-mask", - "pretrained_model": "cl-tohoku/bert-base-japanese", - "pretrained_tokenizer": "cl-tohoku/bert-base-japanese", - } - ), - ) +1. Short sentence with a single masked word. -@pytest.fixture -async def current_runtime(bert_japanese_settings: ModelSettings) -> HuggingFaceRuntime: - runtime = HuggingFaceRuntime(bert_japanese_settings) - await runtime.load() - return runtime +Input: 実際に空が[MASK]のか？ English: Is the sky truly [MASK]? + +Output: ある　ー＞ English: Does the sky truly exist? + +Model: izumi-lab/bert-small-japanese (a 70 MB masked language model +trained on a mask filling task.) +""" -@pytest.fixture -def japanese_text_request(): - # Test sentence: Is the sky really [MASK]? - test_sentence = "実際に空が[MASK]のか？" - # [MASK] = visible - expected_output = "見える" - outputs = [expected_output] +@pytest.mark.parametrize( + "test_sentence,expected_output,model_parameters", + [ + ( + "実際に空が[MASK]のか？", + "ある", + ModelParameters( + extra={ + "task": "fill-mask", + "pretrained_model": "izumi-lab/bert-small-japanese", + "pretrained_tokenizer": "izumi-lab/bert-small-japanese", + } + ), + ) + ], +) +async def test_infer(test_sentence, expected_output, model_parameters, request): + if (not request.config.option.test_hg_tasks) or ( + not request.config.option.test_ja_support + ): + pytest.skip() + + # Get reqs = HuggingfaceRequestCodec.encode_request( {"inputs": [test_sentence]}, use_bytes=False, ) - return reqs, outputs + # Load model + bert_japanese_settings = ModelSettings( + name="model", implementation=HuggingFaceRuntime, parameters=model_parameters + ) + + # Create runtime + runtime = HuggingFaceRuntime(bert_japanese_settings) + await runtime.load() -async def test_infer(current_runtime, japanese_text_request): - reqs, expected_outputs = japanese_text_request - resp = await current_runtime.predict(reqs) + # Generate response + resp = await runtime.predict(reqs) decoded = HuggingfaceRequestCodec.decode_response(resp) - for req, expected_output in zip(list(decoded.items()), expected_outputs): - top_result = req[1][0] - assert top_result["token_str"] == expected_output, "Inference is correct!" + # Get top result + top_result = decoded["output"][0] + assert top_result["token_str"] == expected_output, "Inference is correct!" From 73c60a732159168829b2178946531ac040c5f0ef Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 26 Dec 2023 09:59:01 -0800 Subject: [PATCH 13/49] Finished an inline comment I forgot to fully type out --- .../huggingface/tests/test_tasks/test_japanese_mask_fill.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py index 028c2c72d..28781e174 100644 --- a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -42,7 +42,7 @@ async def test_infer(test_sentence, expected_output, model_parameters, request): ): pytest.skip() - # Get + # Get inferences reqs = HuggingfaceRequestCodec.encode_request( {"inputs": [test_sentence]}, use_bytes=False, From 2715c2bbdf6aec2172f1d4b0dbe959f9c634e481 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 16 Jan 2024 10:02:02 -0800 Subject: [PATCH 14/49] Updated lock and docs --- docs/examples/huggingface/README.bak.ipynb | 815 ++++++++++++++++ docs/examples/huggingface/README.md | 8 +- runtimes/huggingface/poetry.lock | 1021 ++++++++++---------- 3 files changed, 1353 insertions(+), 491 deletions(-) create mode 100755 docs/examples/huggingface/README.bak.ipynb diff --git a/docs/examples/huggingface/README.bak.ipynb b/docs/examples/huggingface/README.bak.ipynb new file mode 100755 index 000000000..b7e69b60b --- /dev/null +++ b/docs/examples/huggingface/README.bak.ipynb @@ -0,0 +1,815 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8c519626", + "metadata": {}, + "source": [ + "# Serving HuggingFace Transformer Models\n", + "\n", + "Out of the box, MLServer supports the deployment and serving of HuggingFace Transformer models with the following features:\n", + "\n", + "- Loading of Transformer Model artifacts from the Hugging Face Hub.\n", + "- Model quantization & optimization using the Hugging Face Optimum library\n", + "- Request batching for GPU optimization (via adaptive batching and request batching)\n", + "\n", + "In this example, we will showcase some of this features using an example model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b5b2588c", + "metadata": {}, + "outputs": [], + "source": [ + "# Import required dependencies\n", + "import requests" + ] + }, + { + "cell_type": "markdown", + "id": "f1fed35e", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "Now that we have trained and serialised our model, we are ready to start serving it.\n", + "For that, the initial step will be to set up a `model-settings.json` that instructs MLServer to load our artifact using the HuggingFace Inference Runtime.\n", + "\n", + "We will show how to add share a task " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6df62443", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"pretrained_model\": \"distilgpt2\"\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "c6a6e8b2", + "metadata": {}, + "source": [ + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "id": "b664c591", + "metadata": {}, + "source": [ + "### Send test inference request\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "759ad7df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': 'eb160c6b-8223-4342-ad92-6ac301a9fa5d',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"generated_text\": \"this is a testnet with 1-3,000-bit nodes as nodes.\"}']}]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"args\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"this is a test\"],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" + ] + }, + { + "cell_type": "markdown", + "id": "0edbcf72", + "metadata": {}, + "source": [ + "### Using Optimum Optimized Models\n", + "\n", + "We can also leverage the Optimum library that allows us to access quantized and optimized models. \n", + "\n", + "We can download pretrained optimized models from the hub if available by enabling the `optimum_model` flag:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6d185281", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"pretrained_model\": \"distilgpt2\",\n", + " \"optimum_model\": true\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "90925ef0", + "metadata": {}, + "source": [ + "Once again, you are able to run the model using the MLServer CLI. As before this needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "6945db96", + "metadata": {}, + "source": [ + "### Send Test Request to Optimum Optimized Model\n", + "\n", + "The request can now be sent using the same request structure but using optimized models for better performance." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "39d8b438", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '9c482c8d-b21e-44b1-8a42-7650a9dc01ef',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"generated_text\": \"this is a test of the \\\\\"safe-code-safe-code-safe-code\\\\\" approach. The method only accepts two parameters as parameters: the code. The parameter \\'unsafe-code-safe-code-safe-code\\' should\"}']}]}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"args\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"this is a test\"],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" + ] + }, + { + "cell_type": "markdown", + "id": "ad88a78f", + "metadata": {}, + "source": [ + "## Testing Supported Tasks\n", + "\n", + "We can support multiple other transformers other than just text generation, below includes examples for a few other tasks supported.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e9ee06c8", + "metadata": {}, + "source": [ + "### Question Answering" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4492dc01", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"question-answering\"\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "9abf39e0", + "metadata": {}, + "source": [ + "Once again, you are able to run the model using the MLServer CLI.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d7aaf365", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '4efac938-86d8-41a1-b78f-7690b2dcf197',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"score\": 0.9869915843009949, \"start\": 12, \"end\": 18, \"answer\": \"Seldon\"}']}]}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"question\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"what is your name?\"],\n", + " },\n", + " {\n", + " \"name\": \"context\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"Hello, I am Seldon, how is it going\"],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" + ] + }, + { + "cell_type": "markdown", + "id": "5aa51356", + "metadata": {}, + "source": [ + "### Sentiment Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8e70c7d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-classification\"\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "ed336525", + "metadata": {}, + "source": [ + "Once again, you are able to run the model using the MLServer CLI.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2f704413", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"label\": \"NEGATIVE\", \"score\": 0.9996137022972107}']}]}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"args\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"This is terrible!\"],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" + ] + }, + { + "cell_type": "markdown", + "id": "f1d4b24a-4c09-4743-a086-6f8b143711ad", + "metadata": {}, + "source": [ + "### Masked Language Modeling\n", + "\n", + "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "63e368ee-a5ef-44b7-aab8-cafd30ab227a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"fill-mask\",\n", + " \"pretrained_model\": \"cl-tohoku/bert-base-japanese\",\n", + " \"pretrained_tokenizer\": \"cl-tohoku/bert-base-japanese\"\n", + " }\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "72b05244-f52f-41c0-a1bc-a5d11d1c75a0", + "metadata": {}, + "source": [ + "Using the shell to start mlserver like so,\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b619c852-f35d-4506-91e1-0a1e3bcb7b8b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'実際 に 空 が 見える の か?'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlserver_huggingface.codecs import HuggingfaceRequestCodec\n", + "import json\n", + "\n", + "# Test sentence: Is the sky really [MASK]?\n", + "test_sentence = \"実際に空が[MASK]のか？\"\n", + "# [MASK] = visible\n", + "expected_output = \"見える\"\n", + "\n", + "inference_request = HuggingfaceRequestCodec.encode_request(\n", + " {\"inputs\": [test_sentence]},\n", + " use_bytes=False,\n", + ")\n", + "json.dumps(inference_request.dict())\n", + "response = requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request.dict()).json()\n", + "json.loads(response['outputs'][0]['data'][0])[\"sequence\"]" + ] + }, + { + "cell_type": "markdown", + "id": "fe6655d9", + "metadata": {}, + "source": [ + "## GPU Acceleration\n", + "\n", + "We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters\n", + "\n", + "### Testing with CPU\n", + "\n", + "We first test the time taken with the device=-1 which configures CPU by default" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "827472eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"max_batch_size\": 128,\n", + " \"max_batch_time\": 1,\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"device\": -1\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "eceb4d12", + "metadata": {}, + "source": [ + "Once again, you are able to run the model using the MLServer CLI.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "888501c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time: 66.42268538899953\n" + ] + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"text_inputs\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"This is a generation for the work\" for i in range(512)],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "# Benchmark time\n", + "import time\n", + "start_time = time.monotonic()\n", + "\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request)\n", + "\n", + "print(f\"Elapsed time: {time.monotonic() - start_time}\")" + ] + }, + { + "cell_type": "markdown", + "id": "92756662", + "metadata": {}, + "source": [ + "We can see that it takes 81 seconds which is 8 times longer than the gpu example below." + ] + }, + { + "cell_type": "markdown", + "id": "cd2e208c", + "metadata": {}, + "source": [ + "### Testing with GPU\n", + "\n", + "IMPORTANT: Running the code below requries having a machine with GPU configured correctly to work for Tensorflow/Pytorch.\n", + " \n", + "Now we'll run the benchmark with GPU configured, which we can do by setting `device=0`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "032b8f4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"device\": 0\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1b6bd339", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time: 11.27933280000434\n" + ] + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"text_inputs\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"This is a generation for the work\" for i in range(512)],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "# Benchmark time\n", + "import time\n", + "start_time = time.monotonic()\n", + "\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request)\n", + "\n", + "print(f\"Elapsed time: {time.monotonic() - start_time}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c929108c", + "metadata": {}, + "source": [ + "We can see that the elapsed time is 8 times less than the CPU version!" + ] + }, + { + "cell_type": "markdown", + "id": "5d790b37", + "metadata": {}, + "source": [ + "### Adaptive Batching with GPU\n", + "\n", + "We can also see how the adaptive batching capabilities can allow for GPU acceleration by grouping multiple incoming requests so they get processed in GPU batch.\n", + "\n", + "In our case we can enable adaptive batching with the `max_batch_size` which in our case we will set it ot 128.\n", + "\n", + "We will also configure `max_batch_time` which specifies` the maximum amount of time the MLServer orchestrator will wait before sending for inference." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "810a4abe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"max_batch_size\": 128,\n", + " \"max_batch_time\": 1,\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"pretrained_model\": \"distilgpt2\",\n", + " \"device\": 0\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "e063ccb6", + "metadata": {}, + "source": [ + "In order to achieve the throughput required of 50 requests per second, we will use the tool `vegeta` which performs load testing.\n", + "\n", + "We can now see that we are able to see that the requests are batched and we receive 100% success eventhough the requests are sent one-by-one." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1a787909", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requests [total, rate, throughput] 150, 50.34, 22.28\n", + "Duration [total, attack, wait] 6.732s, 2.98s, 3.753s\n", + "Latencies [min, mean, 50, 90, 95, 99, max] 1.975s, 3.168s, 3.22s, 4.065s, 4.183s, 4.299s, 4.318s\n", + "Bytes In [total, mean] 60978, 406.52\n", + "Bytes Out [total, mean] 12300, 82.00\n", + "Success [ratio] 100.00%\n", + "Status Codes [code:count] 200:150 \n", + "Error Set:\n" + ] + } + ], + "source": [ + "%%bash\n", + "jq -ncM '{\"method\": \"POST\", \"header\": {\"Content-Type\": [\"application/json\"] }, \"url\": \"http://localhost:8080/v2/models/transformer/infer\", \"body\": \"{\\\"inputs\\\":[{\\\"name\\\":\\\"text_inputs\\\",\\\"shape\\\":[1],\\\"datatype\\\":\\\"BYTES\\\",\\\"data\\\":[\\\"test\\\"]}]}\" | @base64 }' \\\n", + " | vegeta \\\n", + " -cpus=\"2\" \\\n", + " attack \\\n", + " -duration=\"3s\" \\\n", + " -rate=\"50\" \\\n", + " -format=json \\\n", + " | vegeta \\\n", + " report \\\n", + " -type=text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ddcb458", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/huggingface/README.md b/docs/examples/huggingface/README.md index 993ee6def..a03bfeec8 100644 --- a/docs/examples/huggingface/README.md +++ b/docs/examples/huggingface/README.md @@ -199,9 +199,13 @@ inference_request = { requests.post("http://localhost:8080/v2/models/transformer/infer", json=inference_request).json() ``` -### Masked Language Modeling +### Masked Language Modeling (Optional Japanese Language Example) -We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers. +We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers. For example, after running the normal project build from the root directory with `make install-dev`, we can install the optional Japanese dependencies in dev mode: + +`poetry install -E japanese` + +from the `./runtimes/huggingface` level. ```python %%writefile ./model-settings.json diff --git a/runtimes/huggingface/poetry.lock b/runtimes/huggingface/poetry.lock index fe8161d9e..ad5927d2c 100644 --- a/runtimes/huggingface/poetry.lock +++ b/runtimes/huggingface/poetry.lock @@ -120,48 +120,55 @@ speedups = ["Brotli", "aiodns", "brotlicffi"] [[package]] name = "aiokafka" -version = "0.9.0" +version = "0.10.0" description = "Kafka integration with asyncio" optional = false python-versions = ">=3.8" files = [ - {file = "aiokafka-0.9.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:cc9ccae749f15a0a316d6dca7c1a259524854c1d111d0a981ec9a39416afbe4f"}, - {file = "aiokafka-0.9.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = 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"markdown", - "id": "8c519626", - "metadata": {}, - "source": [ - "# Serving HuggingFace Transformer Models\n", - "\n", - "Out of the box, MLServer supports the deployment and serving of HuggingFace Transformer models with the following features:\n", - "\n", - "- Loading of Transformer Model artifacts from the Hugging Face Hub.\n", - "- Model quantization & optimization using the Hugging Face Optimum library\n", - "- Request batching for GPU optimization (via adaptive batching and request batching)\n", - "\n", - "In this example, we will showcase some of this features using an example model." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b5b2588c", - "metadata": {}, - "outputs": [], - "source": [ - "# Import required dependencies\n", - "import requests" - ] - }, - { - "cell_type": "markdown", - "id": "f1fed35e", - "metadata": {}, - "source": [ - "## Serving\n", - "\n", - "Now that we have trained and serialised our model, we are ready to start serving it.\n", - "For that, the initial step will be to set up a `model-settings.json` that instructs MLServer to load our artifact using the HuggingFace Inference Runtime.\n", - "\n", - "We will show how to add share a task " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6df62443", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"text-generation\",\n", - " \"pretrained_model\": \"distilgpt2\"\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "c6a6e8b2", - "metadata": {}, - "source": [ - "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```\n", - "\n", - "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." - ] - }, - { - "cell_type": "markdown", - "id": "b664c591", - "metadata": {}, - "source": [ - "### Send test inference request\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "759ad7df", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': 'eb160c6b-8223-4342-ad92-6ac301a9fa5d',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"generated_text\": \"this is a testnet with 1-3,000-bit nodes as nodes.\"}']}]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"args\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"this is a test\"],\n", - " }\n", - " ]\n", - "}\n", - "\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" - ] - }, - { - "cell_type": "markdown", - "id": "0edbcf72", - "metadata": {}, - "source": [ - "### Using Optimum Optimized Models\n", - "\n", - "We can also leverage the Optimum library that allows us to access quantized and optimized models. \n", - "\n", - "We can download pretrained optimized models from the hub if available by enabling the `optimum_model` flag:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6d185281", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"text-generation\",\n", - " \"pretrained_model\": \"distilgpt2\",\n", - " \"optimum_model\": true\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "90925ef0", - "metadata": {}, - "source": [ - "Once again, you are able to run the model using the MLServer CLI. As before this needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "6945db96", - "metadata": {}, - "source": [ - "### Send Test Request to Optimum Optimized Model\n", - "\n", - "The request can now be sent using the same request structure but using optimized models for better performance." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "39d8b438", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': '9c482c8d-b21e-44b1-8a42-7650a9dc01ef',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"generated_text\": \"this is a test of the \\\\\"safe-code-safe-code-safe-code\\\\\" approach. The method only accepts two parameters as parameters: the code. The parameter \\'unsafe-code-safe-code-safe-code\\' should\"}']}]}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"args\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"this is a test\"],\n", - " }\n", - " ]\n", - "}\n", - "\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" - ] - }, - { - "cell_type": "markdown", - "id": "ad88a78f", - "metadata": {}, - "source": [ - "## Testing Supported Tasks\n", - "\n", - "We can support multiple other transformers other than just text generation, below includes examples for a few other tasks supported.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "e9ee06c8", - "metadata": {}, - "source": [ - "### Question Answering" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4492dc01", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"question-answering\"\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "9abf39e0", - "metadata": {}, - "source": [ - "Once again, you are able to run the model using the MLServer CLI.\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d7aaf365", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': '4efac938-86d8-41a1-b78f-7690b2dcf197',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"score\": 0.9869915843009949, \"start\": 12, \"end\": 18, \"answer\": \"Seldon\"}']}]}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"question\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"what is your name?\"],\n", - " },\n", - " {\n", - " \"name\": \"context\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"Hello, I am Seldon, how is it going\"],\n", - " }\n", - " ]\n", - "}\n", - "\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" - ] - }, - { - "cell_type": "markdown", - "id": "5aa51356", - "metadata": {}, - "source": [ - "### Sentiment Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "8e70c7d7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"text-classification\"\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "ed336525", - "metadata": {}, - "source": [ - "Once again, you are able to run the model using the MLServer CLI.\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2f704413", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"label\": \"NEGATIVE\", \"score\": 0.9996137022972107}']}]}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"args\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"This is terrible!\"],\n", - " }\n", - " ]\n", - "}\n", - "\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" - ] - }, - { - "cell_type": "markdown", - "id": "f1d4b24a-4c09-4743-a086-6f8b143711ad", - "metadata": {}, - "source": [ - "### Masked Language Modeling\n", - "\n", - "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "63e368ee-a5ef-44b7-aab8-cafd30ab227a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"fill-mask\",\n", - " \"pretrained_model\": \"cl-tohoku/bert-base-japanese\",\n", - " \"pretrained_tokenizer\": \"cl-tohoku/bert-base-japanese\"\n", - " }\n", - " }" - ] - }, - { - "cell_type": "markdown", - "id": "72b05244-f52f-41c0-a1bc-a5d11d1c75a0", - "metadata": {}, - "source": [ - "Using the shell to start mlserver like so,\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```\n", - "we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b619c852-f35d-4506-91e1-0a1e3bcb7b8b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'実際 に 空 が 見える の か?'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlserver_huggingface.codecs import HuggingfaceRequestCodec\n", - "import json\n", - "\n", - "# Test sentence: Is the sky really [MASK]?\n", - "test_sentence = \"実際に空が[MASK]のか？\"\n", - "# [MASK] = visible\n", - "expected_output = \"見える\"\n", - "\n", - "inference_request = HuggingfaceRequestCodec.encode_request(\n", - " {\"inputs\": [test_sentence]},\n", - " use_bytes=False,\n", - ")\n", - "json.dumps(inference_request.dict())\n", - "response = requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request.dict()).json()\n", - "json.loads(response['outputs'][0]['data'][0])[\"sequence\"]" - ] - }, - { - "cell_type": "markdown", - "id": "fe6655d9", - "metadata": {}, - "source": [ - "## GPU Acceleration\n", - "\n", - "We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters\n", - "\n", - "### Testing with CPU\n", - "\n", - "We first test the time taken with the device=-1 which configures CPU by default" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "827472eb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"max_batch_size\": 128,\n", - " \"max_batch_time\": 1,\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"text-generation\",\n", - " \"device\": -1\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "eceb4d12", - "metadata": {}, - "source": [ - "Once again, you are able to run the model using the MLServer CLI.\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "888501c1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time: 66.42268538899953\n" - ] - } - ], - "source": [ - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"text_inputs\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"This is a generation for the work\" for i in range(512)],\n", - " }\n", - " ]\n", - "}\n", - "\n", - "# Benchmark time\n", - "import time\n", - "start_time = time.monotonic()\n", - "\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request)\n", - "\n", - "print(f\"Elapsed time: {time.monotonic() - start_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "92756662", - "metadata": {}, - "source": [ - "We can see that it takes 81 seconds which is 8 times longer than the gpu example below." - ] - }, - { - "cell_type": "markdown", - "id": "cd2e208c", - "metadata": {}, - "source": [ - "### Testing with GPU\n", - "\n", - "IMPORTANT: Running the code below requries having a machine with GPU configured correctly to work for Tensorflow/Pytorch.\n", - " \n", - "Now we'll run the benchmark with GPU configured, which we can do by setting `device=0`" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "032b8f4e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"text-generation\",\n", - " \"device\": 0\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "1b6bd339", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time: 11.27933280000434\n" - ] - } - ], - "source": [ - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"text_inputs\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"This is a generation for the work\" for i in range(512)],\n", - " }\n", - " ]\n", - "}\n", - "\n", - "# Benchmark time\n", - "import time\n", - "start_time = time.monotonic()\n", - "\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request)\n", - "\n", - "print(f\"Elapsed time: {time.monotonic() - start_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "c929108c", - "metadata": {}, - "source": [ - "We can see that the elapsed time is 8 times less than the CPU version!" - ] - }, - { - "cell_type": "markdown", - "id": "5d790b37", - "metadata": {}, - "source": [ - "### Adaptive Batching with GPU\n", - "\n", - "We can also see how the adaptive batching capabilities can allow for GPU acceleration by grouping multiple incoming requests so they get processed in GPU batch.\n", - "\n", - "In our case we can enable adaptive batching with the `max_batch_size` which in our case we will set it ot 128.\n", - "\n", - "We will also configure `max_batch_time` which specifies` the maximum amount of time the MLServer orchestrator will wait before sending for inference." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "810a4abe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"max_batch_size\": 128,\n", - " \"max_batch_time\": 1,\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"text-generation\",\n", - " \"pretrained_model\": \"distilgpt2\",\n", - " \"device\": 0\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "e063ccb6", - "metadata": {}, - "source": [ - "In order to achieve the throughput required of 50 requests per second, we will use the tool `vegeta` which performs load testing.\n", - "\n", - "We can now see that we are able to see that the requests are batched and we receive 100% success eventhough the requests are sent one-by-one." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "1a787909", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requests [total, rate, throughput] 150, 50.34, 22.28\n", - "Duration [total, attack, wait] 6.732s, 2.98s, 3.753s\n", - "Latencies [min, mean, 50, 90, 95, 99, max] 1.975s, 3.168s, 3.22s, 4.065s, 4.183s, 4.299s, 4.318s\n", - "Bytes In [total, mean] 60978, 406.52\n", - "Bytes Out [total, mean] 12300, 82.00\n", - "Success [ratio] 100.00%\n", - "Status Codes [code:count] 200:150 \n", - "Error Set:\n" - ] - } - ], - "source": [ - "%%bash\n", - "jq -ncM '{\"method\": \"POST\", \"header\": {\"Content-Type\": [\"application/json\"] }, \"url\": \"http://localhost:8080/v2/models/transformer/infer\", \"body\": \"{\\\"inputs\\\":[{\\\"name\\\":\\\"text_inputs\\\",\\\"shape\\\":[1],\\\"datatype\\\":\\\"BYTES\\\",\\\"data\\\":[\\\"test\\\"]}]}\" | @base64 }' \\\n", - " | vegeta \\\n", - " -cpus=\"2\" \\\n", - " attack \\\n", - " -duration=\"3s\" \\\n", - " -rate=\"50\" \\\n", - " -format=json \\\n", - " | vegeta \\\n", - " report \\\n", - " -type=text" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0ddcb458", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 8992881d54ea2809d15074e703bd526afc6d4eb8 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 16 Jan 2024 10:09:09 -0800 Subject: [PATCH 16/49] Updated README.ipynb to match README.md --- docs/examples/huggingface/README.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index b7e69b60b..ecc1cbbdf 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -425,13 +425,14 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "f1d4b24a-4c09-4743-a086-6f8b143711ad", "metadata": {}, "source": [ - "### Masked Language Modeling\n", + "### Masked Language Modeling (Optional Japanese Language Example)\n", "\n", - "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." + "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers. For example, after running the normal project build from the root directory with `make install-dev`, we can install the optional Japanese dependencies in dev mode:" ] }, { From e4596e0f7ff50fc717f636992b3a906694416ab9 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 12 Apr 2024 08:04:43 -0700 Subject: [PATCH 17/49] rebuilt lock files with same version of poetry --- poetry.lock | 2 +- runtimes/alibi-detect/poetry.lock | 1175 ++++++++++++----------- runtimes/alibi-explain/poetry.lock | 1402 ++++++++++++++-------------- runtimes/catboost/poetry.lock | 1034 ++++++++++---------- runtimes/huggingface/poetry.lock | 2 +- runtimes/lightgbm/poetry.lock | 688 +++++++------- runtimes/mlflow/poetry.lock | 1264 +++++++++++++------------ runtimes/mllib/poetry.lock | 628 +++++++------ runtimes/sklearn/poetry.lock | 748 ++++++++------- runtimes/xgboost/poetry.lock | 748 ++++++++------- 10 files changed, 3829 insertions(+), 3862 deletions(-) diff --git a/poetry.lock b/poetry.lock index d7cdd948c..8b36b8224 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. [[package]] name = "absl-py" diff --git a/runtimes/alibi-detect/poetry.lock b/runtimes/alibi-detect/poetry.lock index 347986602..7e510e63d 100644 --- a/runtimes/alibi-detect/poetry.lock +++ b/runtimes/alibi-detect/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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<49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 16 Apr 2024 09:45:54 +0000 Subject: [PATCH 18/49] build(deps-dev): bump pytest-cases from 3.8.4 to 3.8.5 (#1691) Bumps [pytest-cases](https://github.com/smarie/python-pytest-cases) from 3.8.4 to 3.8.5. - [Release notes](https://github.com/smarie/python-pytest-cases/releases) - [Changelog](https://github.com/smarie/python-pytest-cases/blob/main/docs/changelog.md) - [Commits](https://github.com/smarie/python-pytest-cases/compare/3.8.4...3.8.5) --- updated-dependencies: - dependency-name: pytest-cases dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- poetry.lock | 8 ++++---- pyproject.toml | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/poetry.lock b/poetry.lock index 4e45923b9..a51c49aa9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -5037,13 +5037,13 @@ testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy [[package]] name = "pytest-cases" -version = "3.8.4" +version = "3.8.5" description = "Separate test code from test cases in pytest." optional = false python-versions = "*" files = [ - {file = "pytest-cases-3.8.4.tar.gz", hash = "sha256:b4cec335156f44f3bcfa85e79f2b34b5fa52e6332a8542ddf3e84fa13faacd76"}, - {file = "pytest_cases-3.8.4-py2.py3-none-any.whl", hash = "sha256:d03575a375f953f35ae6840ee35fd1df9f7b6c54194845f81a0fe3d726a395dc"}, + {file = "pytest-cases-3.8.5.tar.gz", hash = "sha256:c92054187847a7d30d8ab6709fb1670a830e4e19234be700c85c96b6d7102c16"}, + {file = "pytest_cases-3.8.5-py2.py3-none-any.whl", hash = "sha256:0c8e3727f6494e65b47321adbd94c767277c7b5de1bd40dd518a13f7443b1fff"}, ] [package.dependencies] @@ -8079,4 +8079,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "37a493ac928b7298acd9a2ed72637a40dd7666f4091f014b988655e08e6f0015" +content-hash = "2513a87aa83f660815ff8f8586ac6e8d00443e8cfcfabdf7292f5c1eeeb27b7f" diff --git a/pyproject.toml b/pyproject.toml index 079f5ed68..55c5a424b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -80,7 +80,7 @@ grpcio-tools = "1.48.2" pytest = "7.4.4" pytest-asyncio = "0.21.1" pytest-mock = "3.12.0" -pytest-cases = "3.8.4" +pytest-cases = "3.8.5" tox = "4.14.2" docker = "7.0.0" aiohttp = "3.9.3" From ed6ef385111b6e31608a41785e94bc2ba4b04a39 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 16 Apr 2024 09:47:43 +0000 Subject: [PATCH 19/49] build(deps): bump scikit-learn in /runtimes/sklearn (#1693) Bumps [scikit-learn](https://github.com/scikit-learn/scikit-learn) from 1.4.1.post1 to 1.4.2. - [Release notes](https://github.com/scikit-learn/scikit-learn/releases) - [Commits](https://github.com/scikit-learn/scikit-learn/compare/1.4.1.post1...1.4.2) --- updated-dependencies: - dependency-name: scikit-learn dependency-type: direct:production update-type: version-update:semver-patch ... 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[Release notes](https://github.com/Lightning-AI/lightning/releases) - [Commits](https://github.com/Lightning-AI/lightning/compare/2.2.1...2.2.2) --- updated-dependencies: - dependency-name: pytorch-lightning dependency-type: direct:development update-type: version-update:semver-patch ... 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[sqlparse](https://github.com/andialbrecht/sqlparse) from 0.4.4 to 0.5.0. - [Changelog](https://github.com/andialbrecht/sqlparse/blob/master/CHANGELOG) - [Commits](https://github.com/andialbrecht/sqlparse/compare/0.4.4...0.5.0) --- updated-dependencies: - dependency-name: sqlparse dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- runtimes/mlflow/poetry.lock | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/runtimes/mlflow/poetry.lock b/runtimes/mlflow/poetry.lock index 6c47c4e53..070b60c5f 100644 --- a/runtimes/mlflow/poetry.lock +++ b/runtimes/mlflow/poetry.lock @@ -3266,19 +3266,18 @@ sqlcipher = ["sqlcipher3_binary"] [[package]] name = "sqlparse" -version = "0.4.4" +version = "0.5.0" description = "A non-validating SQL parser." optional = false -python-versions = ">=3.5" +python-versions = ">=3.8" files = [ - {file = "sqlparse-0.4.4-py3-none-any.whl", hash = "sha256:5430a4fe2ac7d0f93e66f1efc6e1338a41884b7ddf2a350cedd20ccc4d9d28f3"}, - {file = "sqlparse-0.4.4.tar.gz", hash = "sha256:d446183e84b8349fa3061f0fe7f06ca94ba65b426946ffebe6e3e8295332420c"}, + {file = "sqlparse-0.5.0-py3-none-any.whl", hash = "sha256:c204494cd97479d0e39f28c93d46c0b2d5959c7b9ab904762ea6c7af211c8663"}, + {file = "sqlparse-0.5.0.tar.gz", hash = "sha256:714d0a4932c059d16189f58ef5411ec2287a4360f17cdd0edd2d09d4c5087c93"}, ] [package.extras] -dev = ["build", "flake8"] +dev = ["build", "hatch"] doc = ["sphinx"] -test = ["pytest", "pytest-cov"] [[package]] name = "starlette" From dc93ebad9e989d6718acf6eaf25a7bf85a611669 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 16 Apr 2024 09:48:27 +0000 Subject: [PATCH 22/49] build(deps-dev): bump sqlparse from 0.4.4 to 0.5.0 (#1697) Bumps [sqlparse](https://github.com/andialbrecht/sqlparse) from 0.4.4 to 0.5.0. - [Changelog](https://github.com/andialbrecht/sqlparse/blob/master/CHANGELOG) - [Commits](https://github.com/andialbrecht/sqlparse/compare/0.4.4...0.5.0) --- updated-dependencies: - dependency-name: sqlparse dependency-type: indirect ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- poetry.lock | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/poetry.lock b/poetry.lock index a51c49aa9..bcd1941ce 100644 --- a/poetry.lock +++ b/poetry.lock @@ -6498,19 +6498,18 @@ sqlcipher = ["sqlcipher3_binary"] [[package]] name = "sqlparse" -version = "0.4.4" +version = "0.5.0" description = "A non-validating SQL parser." optional = false -python-versions = ">=3.5" +python-versions = ">=3.8" files = [ - {file = "sqlparse-0.4.4-py3-none-any.whl", hash = "sha256:5430a4fe2ac7d0f93e66f1efc6e1338a41884b7ddf2a350cedd20ccc4d9d28f3"}, - {file = "sqlparse-0.4.4.tar.gz", hash = "sha256:d446183e84b8349fa3061f0fe7f06ca94ba65b426946ffebe6e3e8295332420c"}, + {file = "sqlparse-0.5.0-py3-none-any.whl", hash = "sha256:c204494cd97479d0e39f28c93d46c0b2d5959c7b9ab904762ea6c7af211c8663"}, + {file = "sqlparse-0.5.0.tar.gz", hash = "sha256:714d0a4932c059d16189f58ef5411ec2287a4360f17cdd0edd2d09d4c5087c93"}, ] [package.extras] -dev = ["build", "flake8"] +dev = ["build", "hatch"] doc = ["sphinx"] -test = ["pytest", "pytest-cov"] [[package]] name = "srsly" From c6a9c5d33e62a9347a1f5dc13507f4aac481d5c6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 16 Apr 2024 11:52:28 +0100 Subject: [PATCH 23/49] build(deps): bump optimum from 1.17.1 to 1.18.1 in /runtimes/huggingface (#1690) Bumps [optimum](https://github.com/huggingface/optimum) from 1.17.1 to 1.18.1. - [Release notes](https://github.com/huggingface/optimum/releases) - [Commits](https://github.com/huggingface/optimum/compare/v1.17.1...v1.18.1) --- updated-dependencies: - dependency-name: optimum dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- runtimes/huggingface/poetry.lock | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/runtimes/huggingface/poetry.lock b/runtimes/huggingface/poetry.lock index e6fceb42b..8e9a29afa 100644 --- a/runtimes/huggingface/poetry.lock +++ b/runtimes/huggingface/poetry.lock @@ -2308,13 +2308,13 @@ tests = ["pytest", "pytest-cov", "pytest-pep8"] [[package]] name = "optimum" -version = "1.17.1" +version = "1.18.1" description = "Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality." optional = false python-versions = ">=3.7.0" files = [ - {file = "optimum-1.17.1-py3-none-any.whl", hash = "sha256:508bc55db3c9434f4e8d5a30c39a46ac63c4cdb45bcc5a641b6c1c77cae88d23"}, - {file = "optimum-1.17.1.tar.gz", hash = "sha256:e59af717e8691b11903fe2cfb8c6efd6f6798b0417f3e70d231e578a02448ceb"}, + {file = "optimum-1.18.1-py3-none-any.whl", hash = "sha256:c963b4ea86ebc2fd8bbc8b30d3c0fddef6fd47645954f207c1910993498f6547"}, + {file = "optimum-1.18.1.tar.gz", hash = "sha256:c962cc02d55d698461cbc2e3fcf5522fab83ed846c3f904f5bed3efdc48a702b"}, ] [package.dependencies] @@ -2332,30 +2332,30 @@ packaging = "*" protobuf = {version = ">=3.20.1", optional = true, markers = "extra == \"onnxruntime\""} sympy = "*" torch = ">=1.11" -transformers = {version = ">=4.26.0", extras = ["sentencepiece"]} +transformers = {version = ">=4.26.0,<4.40.0", extras = ["sentencepiece"]} [package.extras] amd = ["optimum-amd"] benchmark = ["evaluate (>=0.2.0)", "optuna", "scikit-learn", "seqeval", "torchvision", "tqdm"] -dev = ["Pillow", "accelerate", "black (>=23.1,<24.0)", "diffusers (>=0.17.0)", "einops", "invisible-watermark", "parameterized", "pytest", "pytest-xdist", "requests", "rjieba", "ruff (==0.1.5)", "sacremoses", "scikit-learn", "timm", "torchaudio", "torchvision"] +dev = ["Pillow", "accelerate", "black (>=23.1,<24.0)", "diffusers (>=0.17.0)", "einops", "invisible-watermark", "parameterized", "pytest (<=8.0.0)", "pytest-xdist", "requests", "rjieba", "ruff (==0.1.5)", "sacremoses", "scikit-learn", "timm", "torchaudio", "torchvision"] diffusers = ["diffusers"] doc-build = ["accelerate"] exporters = ["onnx", "onnxruntime", "timm"] exporters-gpu = ["onnx", "onnxruntime-gpu", "timm"] -exporters-tf = ["h5py", "numpy (<1.24.0)", "onnx", "onnxruntime", "tensorflow (>=2.4,<=2.12.1)", "tf2onnx", "timm"] +exporters-tf = ["h5py", "numpy (<1.24.0)", "onnx", "onnxruntime", "tensorflow (>=2.4,<=2.12.1)", "tf2onnx", "timm", "transformers[sentencepiece] (>=4.26.0,<4.38.0)"] furiosa = ["optimum-furiosa"] graphcore = ["optimum-graphcore"] habana = ["optimum-habana", "transformers (>=4.37.0,<4.38.0)"] intel = ["optimum-intel (>=1.15.0)"] neural-compressor = ["optimum-intel[neural-compressor] (>=1.15.0)"] -neuron = ["optimum-neuron[neuron]"] -neuronx = ["optimum-neuron[neuronx]"] +neuron = ["optimum-neuron[neuron] (>=0.0.20)", "transformers (==4.36.2)"] +neuronx = ["optimum-neuron[neuronx] (>=0.0.20)", "transformers (==4.36.2)"] nncf = ["optimum-intel[nncf] (>=1.15.0)"] onnxruntime = ["datasets (>=1.2.1)", "evaluate", "onnx", "onnxruntime (>=1.11.0)", "protobuf (>=3.20.1)"] onnxruntime-gpu = ["accelerate", "datasets (>=1.2.1)", "evaluate", "onnx", "onnxruntime-gpu (>=1.11.0)", "protobuf (>=3.20.1)"] openvino = ["optimum-intel[openvino] (>=1.15.0)"] quality = ["black (>=23.1,<24.0)", "ruff (==0.1.5)"] -tests = ["Pillow", "accelerate", "diffusers (>=0.17.0)", "einops", "invisible-watermark", "parameterized", "pytest", "pytest-xdist", "requests", "rjieba", "sacremoses", "scikit-learn", "timm", "torchaudio", "torchvision"] +tests = ["Pillow", "accelerate", "diffusers (>=0.17.0)", "einops", "invisible-watermark", "parameterized", "pytest (<=8.0.0)", "pytest-xdist", "requests", "rjieba", "sacremoses", "scikit-learn", "timm", "torchaudio", "torchvision"] [[package]] name = "orjson" From 28e4e754fdefaf7a7590bc1ffa2b72f168c22964 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 16 Apr 2024 10:53:26 +0000 Subject: [PATCH 24/49] build(deps): bump joblib from 1.3.2 to 1.4.0 in /runtimes/sklearn (#1692) Bumps [joblib](https://github.com/joblib/joblib) from 1.3.2 to 1.4.0. - [Release notes](https://github.com/joblib/joblib/releases) - [Changelog](https://github.com/joblib/joblib/blob/main/CHANGES.rst) - [Commits](https://github.com/joblib/joblib/compare/1.3.2...1.4.0) --- updated-dependencies: - dependency-name: joblib dependency-type: direct:production update-type: version-update:semver-minor ... 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{version = ">=1.22.4,<2", markers = "python_version < \"3.11\""}, - {version = ">=1.23.2,<2", markers = "python_version == \"3.11\""}, + {version = ">=1.22.4", markers = "python_version < \"3.11\""}, + {version = ">=1.23.2", markers = "python_version == \"3.11\""}, ] python-dateutil = ">=2.8.2" pytz = ">=2020.1" From b1e7c14340a8409f248ff360e056ad6566ba9e35 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 16 Apr 2024 10:59:53 -0400 Subject: [PATCH 26/49] Fixed merge conflict in README --- docs/examples/huggingface/README.md | 27 ++++++++++----------------- 1 file changed, 10 insertions(+), 17 deletions(-) diff --git a/docs/examples/huggingface/README.md b/docs/examples/huggingface/README.md index ad0260e60..81a614669 100644 --- a/docs/examples/huggingface/README.md +++ b/docs/examples/huggingface/README.md @@ -259,8 +259,17 @@ requests.post( "http://localhost:8080/v2/models/transformer/infer", json=inference_request ).json() ``` +``` +{'model_name': 'transformer', + 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b', + 'parameters': {}, + 'outputs': [{'name': 'output', + 'shape': [1, 1], + 'datatype': 'BYTES', + 'parameters': {'content_type': 'hg_jsonlist'}, + 'data': ['{"label": "NEGATIVE", "score": 0.9996137022972107}']}]} +``` -<<<<<<< HEAD ### Masked Language Modeling (Optional Japanese Language Example) We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers. For example, after running the normal project build from the root directory with `make install-dev`, we can install the optional Japanese dependencies in dev mode: @@ -305,22 +314,6 @@ json.dumps(inference_request.dict()) response = requests.post("http://localhost:8080/v2/models/transformer/infer", json=inference_request.dict()).json() json.loads(response['outputs'][0]['data'][0])["sequence"] ``` -======= - - - - {'model_name': 'transformer', - 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b', - 'parameters': {}, - 'outputs': [{'name': 'output', - 'shape': [1, 1], - 'datatype': 'BYTES', - 'parameters': {'content_type': 'hg_jsonlist'}, - 'data': ['{"label": "NEGATIVE", "score": 0.9996137022972107}']}]} - - - ->>>>>>> upstream/master ## GPU Acceleration We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters From 7f21db5f34f1cef2b6c42ac17b979f02d15f58d9 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 16 Apr 2024 11:07:30 -0400 Subject: 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["Sphinx", "repoze.sphinx.autointerface", "sphinx_rtd_theme"] +docs = ["Sphinx", "repoze.sphinx.autointerface", "sphinx-rtd-theme"] test = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] +[extras] +japanese = ["fugashi", "ipadic", "unidic", "unidic-lite"] + [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "1f69e6b02610ab08654a7f7ac5bdfde9ecac54f944bf253f7a23fc80bf60481c" +content-hash = "b7c6fe48d883917e5bb23cb71e8277142ef3ae683fbf05b451018b8b9e1e10d0" diff --git a/runtimes/huggingface/pyproject.toml b/runtimes/huggingface/pyproject.toml index 1a65feabb..4769e6c2f 100644 --- a/runtimes/huggingface/pyproject.toml +++ b/runtimes/huggingface/pyproject.toml @@ -14,6 +14,10 @@ tensorflow = "*" pillow = "*" optimum = {extras = ["onnxruntime"], version = ">=1.4,<2.0"} pydantic = "<2.0.0" +fugashi = {version = "^1.3.0", optional = true} +unidic = {version = "^1.1.0", optional = true} +unidic-lite = {version = "^1.0.8", optional = true} +ipadic = {version = "^1.0.0", optional = true} [tool.poetry.group.dev.dependencies] mlserver = {path = "../..", develop = true} @@ -24,3 +28,6 @@ tensorflow = ">=2.12,<2.17" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" + +[tool.poetry.extras] +japanese = ["fugashi", "unidic", "unidic-lite", "ipadic"] \ No newline at end of file From 32d16e81bb3cd337bb92632b7ffab3bb1565cb7c Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 12 Dec 2023 19:25:25 +0000 Subject: [PATCH 29/49] Added unit test for Japanese server --- .../test_tasks/test_japanese_mask_fill.py | 47 +++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py new file mode 100644 index 000000000..582f9f270 --- /dev/null +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -0,0 +1,47 @@ +import pytest +from mlserver.types import InferenceRequest, RequestInput, Parameters +from mlserver.settings import ModelSettings, ModelParameters +from mlserver_huggingface import HuggingFaceRuntime +from mlserver_huggingface.codecs import HuggingfaceRequestCodec + + +test_sentence = "実際に空が[MASK]のか？" + +@pytest.fixture +def current_model_settings(): + # if not request.config.option.test_hg_tasks: + # pytest.skip() + return ModelSettings( + name="model", + implementation=HuggingFaceRuntime, + parameters=ModelParameters( + extra={ + "task": "fill-mask", + "pretrained_model": "cl-tohoku/bert-base-japanese", + "pretrained_tokenizer": "cl-tohoku/bert-base-japanese", + } + ), + ) + +@pytest.fixture +async def current_runtime(current_model_settings: ModelSettings) -> HuggingFaceRuntime: + runtime = HuggingFaceRuntime(current_model_settings) + await runtime.load() + return runtime + +@pytest.fixture +def japanese_text_request(): + inputs = [ + test_sentence + ] + req = HuggingfaceRequestCodec.encode_request( + {"inputs" : inputs}, + use_bytes=False, + ) + return req + +async def test_infer(current_runtime, japanese_text_request): + resp = await current_runtime.predict(japanese_text_request) + decoded = HuggingfaceRequestCodec.decode_response(resp) + top_result = list(decoded.items())[0][1][0] + assert top_result['token_str'] == "見える", "Inference is incorrect!" \ No newline at end of file From f6a39011173dbf400257c00e14f673cf304698cf Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 12 Dec 2023 19:35:10 +0000 Subject: [PATCH 30/49] linted --- .../test_tasks/test_japanese_mask_fill.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py index 582f9f270..bdf3c8cfc 100644 --- a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -1,5 +1,4 @@ import pytest -from mlserver.types import InferenceRequest, RequestInput, Parameters from mlserver.settings import ModelSettings, ModelParameters from mlserver_huggingface import HuggingFaceRuntime from mlserver_huggingface.codecs import HuggingfaceRequestCodec @@ -7,10 +6,11 @@ test_sentence = "実際に空が[MASK]のか？" + @pytest.fixture -def current_model_settings(): - # if not request.config.option.test_hg_tasks: - # pytest.skip() +def current_model_settings(request) -> ModelSettings: + if not request.config.option.test_hg_tasks: + pytest.skip() return ModelSettings( name="model", implementation=HuggingFaceRuntime, @@ -23,25 +23,26 @@ def current_model_settings(): ), ) + @pytest.fixture async def current_runtime(current_model_settings: ModelSettings) -> HuggingFaceRuntime: runtime = HuggingFaceRuntime(current_model_settings) await runtime.load() return runtime + @pytest.fixture def japanese_text_request(): - inputs = [ - test_sentence - ] + inputs = [test_sentence] req = HuggingfaceRequestCodec.encode_request( - {"inputs" : inputs}, + {"inputs": inputs}, use_bytes=False, ) return req + async def test_infer(current_runtime, japanese_text_request): resp = await current_runtime.predict(japanese_text_request) decoded = HuggingfaceRequestCodec.decode_response(resp) top_result = list(decoded.items())[0][1][0] - assert top_result['token_str'] == "見える", "Inference is incorrect!" \ No newline at end of file + assert top_result["token_str"] == "見える", "Inference is incorrect!" From 47bd2daaff0beda5589c570ab594daca7d4d37aa Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Thu, 14 Dec 2023 00:35:38 +0000 Subject: [PATCH 31/49] Addressed style comments; made dependencies optional --- runtimes/huggingface/pyproject.toml | 8 +++---- runtimes/huggingface/tests/conftest.py | 1 + .../test_tasks/test_japanese_mask_fill.py | 21 ++++++++++++------- 3 files changed, 18 insertions(+), 12 deletions(-) diff --git a/runtimes/huggingface/pyproject.toml b/runtimes/huggingface/pyproject.toml index 4769e6c2f..62d8f22a6 100644 --- a/runtimes/huggingface/pyproject.toml +++ b/runtimes/huggingface/pyproject.toml @@ -25,9 +25,9 @@ torch = "2.2.2" transformers = ">=4.30,<5.0" tensorflow = ">=2.12,<2.17" +[tool.poetry.extras] +japanese = ["fugashi", "unidic", "unidic-lite", "ipadic"] + [build-system] requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" - -[tool.poetry.extras] -japanese = ["fugashi", "unidic", "unidic-lite", "ipadic"] \ No newline at end of file +build-backend = "poetry.core.masonry.api" \ No newline at end of file diff --git a/runtimes/huggingface/tests/conftest.py b/runtimes/huggingface/tests/conftest.py index 7b6c07998..02744eef2 100644 --- a/runtimes/huggingface/tests/conftest.py +++ b/runtimes/huggingface/tests/conftest.py @@ -9,6 +9,7 @@ # if not provide this command argument, task tests skiped def pytest_addoption(parser): parser.addoption("--test-hg-tasks", action="store_true", default=False) + parser.addoption("--test-ja-support", action="store_true", default=False) @pytest.fixture() diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py index bdf3c8cfc..c37ab3b80 100644 --- a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -4,13 +4,13 @@ from mlserver_huggingface.codecs import HuggingfaceRequestCodec -test_sentence = "実際に空が[MASK]のか？" - - @pytest.fixture -def current_model_settings(request) -> ModelSettings: - if not request.config.option.test_hg_tasks: +def bert_japanese_settings(request) -> ModelSettings: + if (not request.config.option.test_hg_tasks) or ( + not request.config.option.test_ja_support + ): pytest.skip() + return ModelSettings( name="model", implementation=HuggingFaceRuntime, @@ -25,14 +25,17 @@ def current_model_settings(request) -> ModelSettings: @pytest.fixture -async def current_runtime(current_model_settings: ModelSettings) -> HuggingFaceRuntime: - runtime = HuggingFaceRuntime(current_model_settings) +async def current_runtime(bert_japanese_settings: ModelSettings) -> HuggingFaceRuntime: + runtime = HuggingFaceRuntime(bert_japanese_settings) await runtime.load() return runtime @pytest.fixture def japanese_text_request(): + # Test sentence: Is the sky really [MASK]? + test_sentence = "実際に空が[MASK]のか？" + inputs = [test_sentence] req = HuggingfaceRequestCodec.encode_request( {"inputs": inputs}, @@ -42,7 +45,9 @@ def japanese_text_request(): async def test_infer(current_runtime, japanese_text_request): + # [MASK] = visible + expected_output = "見える" resp = await current_runtime.predict(japanese_text_request) decoded = HuggingfaceRequestCodec.decode_response(resp) top_result = list(decoded.items())[0][1][0] - assert top_result["token_str"] == "見える", "Inference is incorrect!" + assert top_result["token_str"] == expected_output, "Inference is correct!" From 188b3692279ff2ca570f154afa0554ccd3cef64f Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 22:57:55 +0000 Subject: [PATCH 32/49] Updated README.md and unit test --- bert-japanese-small/model-settings.json | 11 ++ docs/examples/huggingface/README.ipynb | 104 +++++++++++++++++- docs/examples/huggingface/README.md | 40 +++++++ docs/examples/huggingface/model-settings.json | 11 ++ runtimes/huggingface/poetry.lock | 16 ++- runtimes/huggingface/pyproject.toml | 1 + .../test_tasks/test_japanese_mask_fill.py | 9 +- 7 files changed, 183 insertions(+), 9 deletions(-) create mode 100644 bert-japanese-small/model-settings.json create mode 100644 docs/examples/huggingface/model-settings.json diff --git a/bert-japanese-small/model-settings.json b/bert-japanese-small/model-settings.json new file mode 100644 index 000000000..54a2809a6 --- /dev/null +++ b/bert-japanese-small/model-settings.json @@ -0,0 +1,11 @@ +{ + "name": "transformer", + "implementation": "mlserver_huggingface.runtime.HuggingFaceRuntime", + "parameters": { + "extra": { + "task": "fill-mask", + "pretrained_model": "izumi-lab/bert-small-japanese", + "pretrained_tokenizer": "izumi-lab/bert-small-japanese" + } + } + } \ No newline at end of file diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index aee20b946..b3b3f2730 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "id": "b5b2588c", "metadata": {}, "outputs": [], @@ -437,6 +437,102 @@ ").json()" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9277268a", + "metadata": {}, + "source": [ + "### Masked Language Modeling\n", + "\n", + "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "11117091", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"fill-mask\",\n", + " \"pretrained_model\": \"izumi-lab/bert-small-japanese\",\n", + " \"pretrained_tokenizer\": \"izumi-lab/bert-small-japanese\"\n", + " }\n", + " }\n", + "}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a3c359e2", + "metadata": {}, + "source": [ + "Using the shell to start mlserver like so,\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "c8739303", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Response:\n", + "{'model_name': 'transformer', 'id': '9e966d8d-b43d-4ab4-8d47-90e367196233', 'parameters': {}, 'outputs': [{'name': 'output', 'shape': [5, 1], 'datatype': 'BYTES', 'parameters': {'content_type': 'hg_jsonlist'}, 'data': ['{\"score\": 0.3277095854282379, \"token\": 11819, \"token_str\": \"\\\\u3042\\\\u308b\", \"sequence\": \"\\\\u5b9f\\\\u969b \\\\u306b \\\\u7a7a \\\\u304c \\\\u3042\\\\u308b \\\\u306e \\\\u304b?\"}', '{\"score\": 0.10271108895540237, \"token\": 14656, \"token_str\": \"\\\\u898b\\\\u3048\\\\u308b\", \"sequence\": \"\\\\u5b9f\\\\u969b \\\\u306b \\\\u7a7a \\\\u304c \\\\u898b\\\\u3048\\\\u308b \\\\u306e \\\\u304b?\"}', '{\"score\": 0.08325661718845367, \"token\": 11835, \"token_str\": \"\\\\u306a\\\\u3044\", \"sequence\": \"\\\\u5b9f\\\\u969b \\\\u306b \\\\u7a7a \\\\u304c \\\\u306a\\\\u3044 \\\\u306e \\\\u304b?\"}', '{\"score\": 0.036131054162979126, \"token\": 18413, \"token_str\": \"\\\\u6b63\\\\u3057\\\\u3044\", \"sequence\": \"\\\\u5b9f\\\\u969b \\\\u306b \\\\u7a7a \\\\u304c \\\\u6b63\\\\u3057\\\\u3044 \\\\u306e \\\\u304b?\"}', '{\"score\": 0.029351236298680305, \"token\": 11820, \"token_str\": \"\\\\u3044\\\\u308b\", \"sequence\": \"\\\\u5b9f\\\\u969b \\\\u306b \\\\u7a7a \\\\u304c \\\\u3044\\\\u308b \\\\u306e \\\\u304b?\"}']}]}\n", + "Data:\n", + "{'score': 0.3277095854282379, 'token': 11819, 'token_str': 'ある', 'sequence': '実際 に 空 が ある の か?'}\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "# Test sentence: Is the sky really [MASK]?\n", + "test_sentence = \"実際に空が[MASK]のか？\"\n", + "# [MASK] = visible\n", + "expected_output = \"見える\"\n", + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"args\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\" : [test_sentence]\n", + " }\n", + " ]\n", + "}\n", + "response = requests.post(\n", + " \"http://localhost:8080/v2/models/transformer/infer\", json=inference_request\n", + ").json()\n", + "print(\"Response:\")\n", + "print(response)\n", + "print(\"Data:\")\n", + "print(json.loads(response['outputs'][0]['data'][0]))" + ] + }, { "cell_type": "markdown", "id": "fe6655d9", @@ -726,9 +822,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "mlserver_env", "language": "python", - "name": "python3" + "name": "mlserver_env" }, "language_info": { "codemirror_mode": { @@ -740,7 +836,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.8" + "version": "3.9.5" } }, "nbformat": 4, diff --git a/docs/examples/huggingface/README.md b/docs/examples/huggingface/README.md index b4c3cd1c9..cca0740c6 100644 --- a/docs/examples/huggingface/README.md +++ b/docs/examples/huggingface/README.md @@ -274,6 +274,46 @@ requests.post( +### Masked Language Modeling + +We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers. + +```python +%%writefile ./model-settings.json +{ + "name": "model", + "implementation": "mlserver_huggingface.runtime.HuggingFaceRuntime", + "parameters": { + "extra": { + "task": "fill-mask", + "pretrained_model": "cl-tohoku/bert-base-japanese", + "pretrained_tokenizer": "cl-tohoku/bert-base-japanese" + } + } +} +``` +Using the shell to start mlserver like so, +```shell +mlserver start . +``` +we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens. +```python +# Test sentence: Is the sky really [MASK]? +test_sentence = "実際に空が[MASK]のか？" +# [MASK] = visible +expected_output = "見える" +inference_request = { + "inputs": [ + test_sentence + ] +} +``` +``` +Response: +{'model_name': 'transformer', 'id': '9e966d8d-b43d-4ab4-8d47-90e367196233', 'parameters': {}, 'outputs': [{'name': 'output', 'shape': [5, 1], 'datatype': 'BYTES', 'parameters': {'content_type': 'hg_jsonlist'}, 'data': ['{"score": 0.3277095854282379, "token": 11819, "token_str": "\\u3042\\u308b", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u3042\\u308b \\u306e \\u304b?"}', '{"score": 0.10271108895540237, "token": 14656, "token_str": "\\u898b\\u3048\\u308b", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u898b\\u3048\\u308b \\u306e \\u304b?"}', '{"score": 0.08325661718845367, "token": 11835, "token_str": "\\u306a\\u3044", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u306a\\u3044 \\u306e \\u304b?"}', '{"score": 0.036131054162979126, "token": 18413, "token_str": "\\u6b63\\u3057\\u3044", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u6b63\\u3057\\u3044 \\u306e \\u304b?"}', '{"score": 0.029351236298680305, "token": 11820, "token_str": "\\u3044\\u308b", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u3044\\u308b \\u306e \\u304b?"}']}]} +Data: +{'score': 0.3277095854282379, 'token': 11819, 'token_str': 'ある', 'sequence': '実際 に 空 が ある の か?'} +``` ## GPU Acceleration We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters diff --git a/docs/examples/huggingface/model-settings.json b/docs/examples/huggingface/model-settings.json new file mode 100644 index 000000000..2e3298a75 --- /dev/null +++ b/docs/examples/huggingface/model-settings.json @@ -0,0 +1,11 @@ +{ + "name": "transformer", + "implementation": "mlserver_huggingface.runtime.HuggingFaceRuntime", + "parameters": { + "extra": { + "task": "fill-mask", + "pretrained_model": "izumi-lab/bert-small-japanese", + "pretrained_tokenizer": "izumi-lab/bert-small-japanese" + } + } +} diff --git a/runtimes/huggingface/poetry.lock b/runtimes/huggingface/poetry.lock index 82dba0e1e..38c05a1e3 100644 --- a/runtimes/huggingface/poetry.lock +++ b/runtimes/huggingface/poetry.lock @@ -3588,6 +3588,20 @@ files = [ [package.extras] tests = ["pytest", "pytest-cov"] +[[package]] +name = "tf-keras" +version = "2.16.0" +description = "Deep learning for humans." +optional = false +python-versions = ">=3.9" +files = [ + {file = "tf_keras-2.16.0-py3-none-any.whl", hash = "sha256:b2ad0541fa7d9e92c4b7a1b96593377afb58aaff374299a6ca6be1a42f51d899"}, + {file = "tf_keras-2.16.0.tar.gz", hash = "sha256:db53891f1ac98197c2acced98cdca8c06ba8255655a6cb7eb95ed49676118280"}, +] + +[package.dependencies] +tensorflow = ">=2.16,<2.17" + [[package]] name = "tokenizers" version = "0.15.2" @@ -4472,4 +4486,4 @@ japanese = ["fugashi", "ipadic", "unidic", "unidic-lite"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "b7c6fe48d883917e5bb23cb71e8277142ef3ae683fbf05b451018b8b9e1e10d0" +content-hash = "3bd261476b6a5292615504cc52e9f4d00e53deb84fc1319fa84877cfd2929f86" diff --git a/runtimes/huggingface/pyproject.toml b/runtimes/huggingface/pyproject.toml index 62d8f22a6..decc90bb7 100644 --- a/runtimes/huggingface/pyproject.toml +++ b/runtimes/huggingface/pyproject.toml @@ -12,6 +12,7 @@ python = ">=3.9,<3.12" mlserver = "*" tensorflow = "*" pillow = "*" +tf-keras = "<3.0" optimum = {extras = ["onnxruntime"], version = ">=1.4,<2.0"} pydantic = "<2.0.0" fugashi = {version = "^1.3.0", optional = true} diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py index c37ab3b80..4eeaf3c5d 100644 --- a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -35,19 +35,20 @@ async def current_runtime(bert_japanese_settings: ModelSettings) -> HuggingFaceR def japanese_text_request(): # Test sentence: Is the sky really [MASK]? test_sentence = "実際に空が[MASK]のか？" + # [MASK] = visible + expected_output = "見える" inputs = [test_sentence] req = HuggingfaceRequestCodec.encode_request( {"inputs": inputs}, use_bytes=False, ) - return req + return req, expected_output async def test_infer(current_runtime, japanese_text_request): - # [MASK] = visible - expected_output = "見える" - resp = await current_runtime.predict(japanese_text_request) + req, expected_output = japanese_text_request + resp = await current_runtime.predict(req) decoded = HuggingfaceRequestCodec.decode_response(resp) top_result = list(decoded.items())[0][1][0] assert top_result["token_str"] == expected_output, "Inference is correct!" From 7fa5d0ca7225d76c8d6773babed16365431c61a1 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 15:22:30 -0800 Subject: [PATCH 33/49] Updated docs --- docs/examples/huggingface/README.ipynb | 285 +++++++++---------------- docs/examples/huggingface/README.md | 13 +- 2 files changed, 105 insertions(+), 193 deletions(-) diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index b3b3f2730..d2a09c08e 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -23,6 +23,7 @@ "metadata": {}, "outputs": [], "source": [ + "!pip install requests\n", "# Import required dependencies\n", "import requests" ] @@ -47,18 +48,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "6df62443", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -97,28 +90,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "759ad7df", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': 'eb160c6b-8223-4342-ad92-6ac301a9fa5d',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"generated_text\": \"this is a testnet with 1-3,000-bit nodes as nodes.\"}']}]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -150,18 +125,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "6d185281", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -201,28 +168,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "39d8b438", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': '9c482c8d-b21e-44b1-8a42-7650a9dc01ef',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"generated_text\": \"this is a test of the \\\\\"safe-code-safe-code-safe-code\\\\\" approach. The method only accepts two parameters as parameters: the code. The parameter \\'unsafe-code-safe-code-safe-code\\' should\"}']}]}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -261,18 +210,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "4492dc01", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -300,30 +241,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "d7aaf365", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': '4efac938-86d8-41a1-b78f-7690b2dcf197',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"score\": 0.9869915843009949, \"start\": 12, \"end\": 18, \"answer\": \"Seldon\"}']}]}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -357,18 +280,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "8e70c7d7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -396,30 +311,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "2f704413", "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"label\": \"NEGATIVE\", \"score\": 0.9996137022972107}']}]}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -533,6 +430,73 @@ "print(json.loads(response['outputs'][0]['data'][0]))" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "545307aa", + "metadata": {}, + "source": [ + "### Masked Language Modeling\n", + "\n", + "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86ed27dc", + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"model\",\n", + " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"fill-mask\",\n", + " \"pretrained_model\": \"cl-tohoku/bert-base-japanese\",\n", + " \"pretrained_tokenizer\": \"cl-tohoku/bert-base-japanese\"\n", + " }\n", + " }\n", + "}" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f4d34159", + "metadata": {}, + "source": [ + "Using the shell to start mlserver like so,\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d981304d", + "metadata": {}, + "outputs": [], + "source": [ + "from mlserver_huggingface import HuggingFaceRuntime\n", + "# Test sentence: Is the sky really [MASK]?\n", + "test_sentence = \"実際に空が[MASK]のか？\"\n", + "# [MASK] = visible\n", + "expected_output = \"見える\"\n", + "\n", + "\n", + "inference_request = HuggingfaceRequestCodec.encode_request(\n", + " {\"inputs\": [test_sentence]},\n", + " use_bytes=False,\n", + ")\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" + ] + }, { "cell_type": "markdown", "id": "fe6655d9", @@ -549,18 +513,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "827472eb", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -591,18 +547,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "888501c1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time: 66.42268538899953\n" - ] - } - ], + "outputs": [], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -649,18 +597,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "032b8f4e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -677,18 +617,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "1b6bd339", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time: 11.27933280000434\n" - ] - } - ], + "outputs": [], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -737,18 +669,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "810a4abe", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], + "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -778,25 +702,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "1a787909", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requests [total, rate, throughput] 150, 50.34, 22.28\n", - "Duration [total, attack, wait] 6.732s, 2.98s, 3.753s\n", - "Latencies [min, mean, 50, 90, 95, 99, max] 1.975s, 3.168s, 3.22s, 4.065s, 4.183s, 4.299s, 4.318s\n", - "Bytes In [total, mean] 60978, 406.52\n", - "Bytes Out [total, mean] 12300, 82.00\n", - "Success [ratio] 100.00%\n", - "Status Codes [code:count] 200:150 \n", - "Error Set:\n" - ] - } - ], + "outputs": [], "source": [ "%%bash\n", "jq -ncM '{\"method\": \"POST\", \"header\": {\"Content-Type\": [\"application/json\"] }, \"url\": \"http://localhost:8080/v2/models/transformer/infer\", \"body\": \"{\\\"inputs\\\":[{\\\"name\\\":\\\"text_inputs\\\",\\\"shape\\\":[1],\\\"datatype\\\":\\\"BYTES\\\",\\\"data\\\":[\\\"test\\\"]}]}\" | @base64 }' \\\n", @@ -822,7 +731,7 @@ ], "metadata": { "kernelspec": { - "display_name": "mlserver_env", + "display_name": "Python 3", "language": "python", "name": "mlserver_env" }, diff --git a/docs/examples/huggingface/README.md b/docs/examples/huggingface/README.md index cca0740c6..dee49417e 100644 --- a/docs/examples/huggingface/README.md +++ b/docs/examples/huggingface/README.md @@ -298,15 +298,18 @@ mlserver start . ``` we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens. ```python +from mlserver_huggingface import HuggingFaceRuntime # Test sentence: Is the sky really [MASK]? test_sentence = "実際に空が[MASK]のか？" # [MASK] = visible expected_output = "見える" -inference_request = { - "inputs": [ - test_sentence - ] -} + + +inference_request = HuggingfaceRequestCodec.encode_request( + {"inputs": [test_sentence]}, + use_bytes=False, +) +requests.post("http://localhost:8080/v2/models/transformer/infer", json=inference_request).json() ``` ``` Response: From d75599064e9fade38f86959cadef8985f02f7dc6 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 15:54:11 -0800 Subject: [PATCH 34/49] Working test in docs --- docs/examples/huggingface/README.ipynb | 8 -------- docs/examples/huggingface/README.md | 9 ++++++--- 2 files changed, 6 insertions(+), 11 deletions(-) diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index d2a09c08e..8893b7485 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -719,14 +719,6 @@ " report \\\n", " -type=text" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0ddcb458", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/examples/huggingface/README.md b/docs/examples/huggingface/README.md index dee49417e..3e3b5c25a 100644 --- a/docs/examples/huggingface/README.md +++ b/docs/examples/huggingface/README.md @@ -298,18 +298,21 @@ mlserver start . ``` we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens. ```python -from mlserver_huggingface import HuggingFaceRuntime +from mlserver_huggingface.codecs import HuggingfaceRequestCodec +import json + # Test sentence: Is the sky really [MASK]? test_sentence = "実際に空が[MASK]のか？" # [MASK] = visible expected_output = "見える" - inference_request = HuggingfaceRequestCodec.encode_request( {"inputs": [test_sentence]}, use_bytes=False, ) -requests.post("http://localhost:8080/v2/models/transformer/infer", json=inference_request).json() +json.dumps(inference_request.dict()) +response = requests.post("http://localhost:8080/v2/models/transformer/infer", json=inference_request.dict()).json() +json.loads(response['outputs'][0]['data'][0])["sequence"] ``` ``` Response: From bf4d412b6118036cd10ffea31d1506708fd2fe15 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 16:09:52 -0800 Subject: [PATCH 35/49] Generalized test to a list of cases --- .../test_tasks/test_japanese_mask_fill.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py index 4eeaf3c5d..8e97e60a2 100644 --- a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -38,17 +38,19 @@ def japanese_text_request(): # [MASK] = visible expected_output = "見える" - inputs = [test_sentence] - req = HuggingfaceRequestCodec.encode_request( - {"inputs": inputs}, + outputs = [expected_output] + reqs = HuggingfaceRequestCodec.encode_request( + {"inputs": [test_sentence]}, use_bytes=False, ) - return req, expected_output + return reqs, outputs async def test_infer(current_runtime, japanese_text_request): - req, expected_output = japanese_text_request - resp = await current_runtime.predict(req) + reqs, expected_outputs = japanese_text_request + resp = await current_runtime.predict(reqs) decoded = HuggingfaceRequestCodec.decode_response(resp) - top_result = list(decoded.items())[0][1][0] - assert top_result["token_str"] == expected_output, "Inference is correct!" + + for req, expected_output in zip(list(decoded.items()), expected_outputs): + top_result = req[1][0] + assert top_result["token_str"] == expected_output, "Inference is correct!" From 6174e0004cabdc32b27cfd12d421f1f0b9301bd0 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 16:11:12 -0800 Subject: [PATCH 36/49] updated docs --- docs/examples/huggingface/README.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index 8893b7485..1db71537b 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -489,12 +489,13 @@ "# [MASK] = visible\n", "expected_output = \"見える\"\n", "\n", - "\n", "inference_request = HuggingfaceRequestCodec.encode_request(\n", " {\"inputs\": [test_sentence]},\n", " use_bytes=False,\n", ")\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" + "json.dumps(inference_request.dict())\n", + "response = requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request.dict()).json()\n", + "json.loads(response['outputs'][0]['data'][0])[\"sequence\"]" ] }, { From 5f8c8e32ff1cf8c0f50f3b4184388fe97b111a6f Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 16:47:52 -0800 Subject: [PATCH 37/49] Fixed notebook cell outputs --- docs/examples/huggingface/README.ipynb | 381 ++++++++++++------------- 1 file changed, 188 insertions(+), 193 deletions(-) diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index 1db71537b..97bd32198 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "id": "b5b2588c", "metadata": {}, "outputs": [], @@ -48,10 +48,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "6df62443", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -90,10 +98,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "759ad7df", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': 'eb160c6b-8223-4342-ad92-6ac301a9fa5d',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"generated_text\": \"this is a testnet with 1-3,000-bit nodes as nodes.\"}']}]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -125,10 +151,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "6d185281", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -168,10 +202,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "39d8b438", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '9c482c8d-b21e-44b1-8a42-7650a9dc01ef',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"generated_text\": \"this is a test of the \\\\\"safe-code-safe-code-safe-code\\\\\" approach. The method only accepts two parameters as parameters: the code. The parameter \\'unsafe-code-safe-code-safe-code\\' should\"}']}]}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -210,10 +262,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "4492dc01", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -241,12 +301,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "d7aaf365", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '4efac938-86d8-41a1-b78f-7690b2dcf197',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"score\": 0.9869915843009949, \"start\": 12, \"end\": 18, \"answer\": \"Seldon\"}']}]}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -280,10 +358,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "8e70c7d7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -311,12 +397,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "2f704413", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"label\": \"NEGATIVE\", \"score\": 0.9996137022972107}']}]}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -335,20 +439,23 @@ ] }, { - "attachments": {}, "cell_type": "markdown", - "id": "9277268a", + "id": "fe6655d9", "metadata": {}, "source": [ - "### Masked Language Modeling\n", + "## GPU Acceleration\n", + "\n", + "We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters\n", "\n", - "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." + "### Testing with CPU\n", + "\n", + "We first test the time taken with the device=-1 which configures CPU by default" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "11117091", + "execution_count": 17, + "id": "827472eb", "metadata": {}, "outputs": [ { @@ -359,165 +466,6 @@ ] } ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"fill-mask\",\n", - " \"pretrained_model\": \"izumi-lab/bert-small-japanese\",\n", - " \"pretrained_tokenizer\": \"izumi-lab/bert-small-japanese\"\n", - " }\n", - " }\n", - "}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a3c359e2", - "metadata": {}, - "source": [ - "Using the shell to start mlserver like so,\n", - "```shell\n", - "mlserver start .\n", - "```\n", - "we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "c8739303", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Response:\n", - "{'model_name': 'transformer', 'id': '9e966d8d-b43d-4ab4-8d47-90e367196233', 'parameters': {}, 'outputs': [{'name': 'output', 'shape': [5, 1], 'datatype': 'BYTES', 'parameters': {'content_type': 'hg_jsonlist'}, 'data': ['{\"score\": 0.3277095854282379, \"token\": 11819, \"token_str\": \"\\\\u3042\\\\u308b\", \"sequence\": \"\\\\u5b9f\\\\u969b \\\\u306b \\\\u7a7a \\\\u304c \\\\u3042\\\\u308b \\\\u306e \\\\u304b?\"}', '{\"score\": 0.10271108895540237, \"token\": 14656, \"token_str\": \"\\\\u898b\\\\u3048\\\\u308b\", \"sequence\": \"\\\\u5b9f\\\\u969b \\\\u306b \\\\u7a7a \\\\u304c \\\\u898b\\\\u3048\\\\u308b \\\\u306e \\\\u304b?\"}', '{\"score\": 0.08325661718845367, \"token\": 11835, \"token_str\": \"\\\\u306a\\\\u3044\", \"sequence\": \"\\\\u5b9f\\\\u969b \\\\u306b \\\\u7a7a \\\\u304c \\\\u306a\\\\u3044 \\\\u306e \\\\u304b?\"}', '{\"score\": 0.036131054162979126, \"token\": 18413, \"token_str\": \"\\\\u6b63\\\\u3057\\\\u3044\", \"sequence\": \"\\\\u5b9f\\\\u969b \\\\u306b \\\\u7a7a \\\\u304c \\\\u6b63\\\\u3057\\\\u3044 \\\\u306e \\\\u304b?\"}', '{\"score\": 0.029351236298680305, \"token\": 11820, \"token_str\": \"\\\\u3044\\\\u308b\", \"sequence\": \"\\\\u5b9f\\\\u969b \\\\u306b \\\\u7a7a \\\\u304c \\\\u3044\\\\u308b \\\\u306e \\\\u304b?\"}']}]}\n", - "Data:\n", - "{'score': 0.3277095854282379, 'token': 11819, 'token_str': 'ある', 'sequence': '実際 に 空 が ある の か?'}\n" - ] - } - ], - "source": [ - "import json\n", - "\n", - "# Test sentence: Is the sky really [MASK]?\n", - "test_sentence = \"実際に空が[MASK]のか？\"\n", - "# [MASK] = visible\n", - "expected_output = \"見える\"\n", - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"args\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\" : [test_sentence]\n", - " }\n", - " ]\n", - "}\n", - "response = requests.post(\n", - " \"http://localhost:8080/v2/models/transformer/infer\", json=inference_request\n", - ").json()\n", - "print(\"Response:\")\n", - "print(response)\n", - "print(\"Data:\")\n", - "print(json.loads(response['outputs'][0]['data'][0]))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "545307aa", - "metadata": {}, - "source": [ - "### Masked Language Modeling\n", - "\n", - "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86ed27dc", - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"model\",\n", - " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"fill-mask\",\n", - " \"pretrained_model\": \"cl-tohoku/bert-base-japanese\",\n", - " \"pretrained_tokenizer\": \"cl-tohoku/bert-base-japanese\"\n", - " }\n", - " }\n", - "}" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f4d34159", - "metadata": {}, - "source": [ - "Using the shell to start mlserver like so,\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```\n", - "we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d981304d", - "metadata": {}, - "outputs": [], - "source": [ - "from mlserver_huggingface import HuggingFaceRuntime\n", - "# Test sentence: Is the sky really [MASK]?\n", - "test_sentence = \"実際に空が[MASK]のか？\"\n", - "# [MASK] = visible\n", - "expected_output = \"見える\"\n", - "\n", - "inference_request = HuggingfaceRequestCodec.encode_request(\n", - " {\"inputs\": [test_sentence]},\n", - " use_bytes=False,\n", - ")\n", - "json.dumps(inference_request.dict())\n", - "response = requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request.dict()).json()\n", - "json.loads(response['outputs'][0]['data'][0])[\"sequence\"]" - ] - }, - { - "cell_type": "markdown", - "id": "fe6655d9", - "metadata": {}, - "source": [ - "## GPU Acceleration\n", - "\n", - "We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters\n", - "\n", - "### Testing with CPU\n", - "\n", - "We first test the time taken with the device=-1 which configures CPU by default" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "827472eb", - "metadata": {}, - "outputs": [], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -548,10 +496,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "888501c1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time: 66.42268538899953\n" + ] + } + ], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -598,10 +554,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "032b8f4e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -618,10 +582,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "1b6bd339", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time: 11.27933280000434\n" + ] + } + ], "source": [ "inference_request = {\n", " \"inputs\": [\n", @@ -670,10 +642,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "810a4abe", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], "source": [ "%%writefile ./model-settings.json\n", "{\n", @@ -703,10 +683,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "1a787909", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requests [total, rate, throughput] 150, 50.34, 22.28\n", + "Duration [total, attack, wait] 6.732s, 2.98s, 3.753s\n", + "Latencies [min, mean, 50, 90, 95, 99, max] 1.975s, 3.168s, 3.22s, 4.065s, 4.183s, 4.299s, 4.318s\n", + "Bytes In [total, mean] 60978, 406.52\n", + "Bytes Out [total, mean] 12300, 82.00\n", + "Success [ratio] 100.00%\n", + "Status Codes [code:count] 200:150 \n", + "Error Set:\n" + ] + } + ], "source": [ "%%bash\n", "jq -ncM '{\"method\": \"POST\", \"header\": {\"Content-Type\": [\"application/json\"] }, \"url\": \"http://localhost:8080/v2/models/transformer/infer\", \"body\": \"{\\\"inputs\\\":[{\\\"name\\\":\\\"text_inputs\\\",\\\"shape\\\":[1],\\\"datatype\\\":\\\"BYTES\\\",\\\"data\\\":[\\\"test\\\"]}]}\" | @base64 }' \\\n", @@ -726,7 +721,7 @@ "kernelspec": { "display_name": "Python 3", "language": "python", - "name": "mlserver_env" + "name": "python3" }, "language_info": { "codemirror_mode": { From 3fa63e0c5e05af78f95cb609337f4df0a7ccaf0a Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 15 Dec 2023 16:49:50 -0800 Subject: [PATCH 38/49] Fixed notebook cell outputs --- docs/examples/huggingface/README.ipynb | 86 ++++++++++++++++++++++++++ 1 file changed, 86 insertions(+) diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index 97bd32198..9922bd637 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -438,6 +438,92 @@ ").json()" ] }, + { + "cell_type": "markdown", + "id": "f1d4b24a-4c09-4743-a086-6f8b143711ad", + "metadata": {}, + "source": [ + "### Masked Language Modeling\n", + "\n", + "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "63e368ee-a5ef-44b7-aab8-cafd30ab227a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"fill-mask\",\n", + " \"pretrained_model\": \"cl-tohoku/bert-base-japanese\",\n", + " \"pretrained_tokenizer\": \"cl-tohoku/bert-base-japanese\"\n", + " }\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "72b05244-f52f-41c0-a1bc-a5d11d1c75a0", + "metadata": {}, + "source": [ + "Using the shell to start mlserver like so,\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b619c852-f35d-4506-91e1-0a1e3bcb7b8b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'実際 に 空 が 見える の か?'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlserver_huggingface.codecs import HuggingfaceRequestCodec\n", + "import json\n", + "\n", + "# Test sentence: Is the sky really [MASK]?\n", + "test_sentence = \"実際に空が[MASK]のか？\"\n", + "# [MASK] = visible\n", + "expected_output = \"見える\"\n", + "\n", + "inference_request = HuggingfaceRequestCodec.encode_request(\n", + " {\"inputs\": [test_sentence]},\n", + " use_bytes=False,\n", + ")\n", + "json.dumps(inference_request.dict())\n", + "response = requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request.dict()).json()\n", + "json.loads(response['outputs'][0]['data'][0])[\"sequence\"]" + ] + }, { "cell_type": "markdown", "id": "fe6655d9", From aafa4d17148dae23ae33cb84a5b314027d517331 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 26 Dec 2023 09:49:53 -0800 Subject: [PATCH 39/49] Refactored test with pytest.pymark.parameterize. Used a smaller test case. --- .../test_tasks/test_japanese_mask_fill.py | 82 +++++++++++-------- 1 file changed, 46 insertions(+), 36 deletions(-) diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py index 8e97e60a2..028c2c72d 100644 --- a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -4,53 +4,63 @@ from mlserver_huggingface.codecs import HuggingfaceRequestCodec -@pytest.fixture -def bert_japanese_settings(request) -> ModelSettings: - if (not request.config.option.test_hg_tasks) or ( - not request.config.option.test_ja_support - ): - pytest.skip() +""" +Test case summary: - return ModelSettings( - name="model", - implementation=HuggingFaceRuntime, - parameters=ModelParameters( - extra={ - "task": "fill-mask", - "pretrained_model": "cl-tohoku/bert-base-japanese", - "pretrained_tokenizer": "cl-tohoku/bert-base-japanese", - } - ), - ) +1. Short sentence with a single masked word. -@pytest.fixture -async def current_runtime(bert_japanese_settings: ModelSettings) -> HuggingFaceRuntime: - runtime = HuggingFaceRuntime(bert_japanese_settings) - await runtime.load() - return runtime +Input: 実際に空が[MASK]のか？ English: Is the sky truly [MASK]? + +Output: ある　ー＞ English: Does the sky truly exist? + +Model: izumi-lab/bert-small-japanese (a 70 MB masked language model +trained on a mask filling task.) +""" -@pytest.fixture -def japanese_text_request(): - # Test sentence: Is the sky really [MASK]? - test_sentence = "実際に空が[MASK]のか？" - # [MASK] = visible - expected_output = "見える" - outputs = [expected_output] +@pytest.mark.parametrize( + "test_sentence,expected_output,model_parameters", + [ + ( + "実際に空が[MASK]のか？", + "ある", + ModelParameters( + extra={ + "task": "fill-mask", + "pretrained_model": "izumi-lab/bert-small-japanese", + "pretrained_tokenizer": "izumi-lab/bert-small-japanese", + } + ), + ) + ], +) +async def test_infer(test_sentence, expected_output, model_parameters, request): + if (not request.config.option.test_hg_tasks) or ( + not request.config.option.test_ja_support + ): + pytest.skip() + + # Get reqs = HuggingfaceRequestCodec.encode_request( {"inputs": [test_sentence]}, use_bytes=False, ) - return reqs, outputs + # Load model + bert_japanese_settings = ModelSettings( + name="model", implementation=HuggingFaceRuntime, parameters=model_parameters + ) + + # Create runtime + runtime = HuggingFaceRuntime(bert_japanese_settings) + await runtime.load() -async def test_infer(current_runtime, japanese_text_request): - reqs, expected_outputs = japanese_text_request - resp = await current_runtime.predict(reqs) + # Generate response + resp = await runtime.predict(reqs) decoded = HuggingfaceRequestCodec.decode_response(resp) - for req, expected_output in zip(list(decoded.items()), expected_outputs): - top_result = req[1][0] - assert top_result["token_str"] == expected_output, "Inference is correct!" + # Get top result + top_result = decoded["output"][0] + assert top_result["token_str"] == expected_output, "Inference is correct!" From 549f2444164a71e0d3f0beca08fab736b82fb23a Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 26 Dec 2023 09:59:01 -0800 Subject: [PATCH 40/49] Finished an inline comment I forgot to fully type out --- .../huggingface/tests/test_tasks/test_japanese_mask_fill.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py index 028c2c72d..28781e174 100644 --- a/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py +++ b/runtimes/huggingface/tests/test_tasks/test_japanese_mask_fill.py @@ -42,7 +42,7 @@ async def test_infer(test_sentence, expected_output, model_parameters, request): ): pytest.skip() - # Get + # Get inferences reqs = HuggingfaceRequestCodec.encode_request( {"inputs": [test_sentence]}, use_bytes=False, From 0d61961d6ce8733a8ec0dbcb05c719e7c018ffec Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 16 Jan 2024 10:02:02 -0800 Subject: [PATCH 41/49] Updated lock and docs --- docs/examples/huggingface/README.bak.ipynb | 815 +++++++++++++++++++++ docs/examples/huggingface/README.md | 6 +- runtimes/huggingface/poetry.lock | 403 ++++++++++ 3 files changed, 1223 insertions(+), 1 deletion(-) create mode 100755 docs/examples/huggingface/README.bak.ipynb diff --git a/docs/examples/huggingface/README.bak.ipynb b/docs/examples/huggingface/README.bak.ipynb new file mode 100755 index 000000000..b7e69b60b --- /dev/null +++ b/docs/examples/huggingface/README.bak.ipynb @@ -0,0 +1,815 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8c519626", + "metadata": {}, + "source": [ + "# Serving HuggingFace Transformer Models\n", + "\n", + "Out of the box, MLServer supports the deployment and serving of HuggingFace Transformer models with the following features:\n", + "\n", + "- Loading of Transformer Model artifacts from the Hugging Face Hub.\n", + "- Model quantization & optimization using the Hugging Face Optimum library\n", + "- Request batching for GPU optimization (via adaptive batching and request batching)\n", + "\n", + "In this example, we will showcase some of this features using an example model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b5b2588c", + "metadata": {}, + "outputs": [], + "source": [ + "# Import required dependencies\n", + "import requests" + ] + }, + { + "cell_type": "markdown", + "id": "f1fed35e", + "metadata": {}, + "source": [ + "## Serving\n", + "\n", + "Now that we have trained and serialised our model, we are ready to start serving it.\n", + "For that, the initial step will be to set up a `model-settings.json` that instructs MLServer to load our artifact using the HuggingFace Inference Runtime.\n", + "\n", + "We will show how to add share a task " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6df62443", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"pretrained_model\": \"distilgpt2\"\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "c6a6e8b2", + "metadata": {}, + "source": [ + "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "\n", + "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." + ] + }, + { + "cell_type": "markdown", + "id": "b664c591", + "metadata": {}, + "source": [ + "### Send test inference request\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "759ad7df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': 'eb160c6b-8223-4342-ad92-6ac301a9fa5d',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"generated_text\": \"this is a testnet with 1-3,000-bit nodes as nodes.\"}']}]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"args\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"this is a test\"],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" + ] + }, + { + "cell_type": "markdown", + "id": "0edbcf72", + "metadata": {}, + "source": [ + "### Using Optimum Optimized Models\n", + "\n", + "We can also leverage the Optimum library that allows us to access quantized and optimized models. \n", + "\n", + "We can download pretrained optimized models from the hub if available by enabling the `optimum_model` flag:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6d185281", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"pretrained_model\": \"distilgpt2\",\n", + " \"optimum_model\": true\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "90925ef0", + "metadata": {}, + "source": [ + "Once again, you are able to run the model using the MLServer CLI. As before this needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "6945db96", + "metadata": {}, + "source": [ + "### Send Test Request to Optimum Optimized Model\n", + "\n", + "The request can now be sent using the same request structure but using optimized models for better performance." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "39d8b438", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '9c482c8d-b21e-44b1-8a42-7650a9dc01ef',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"generated_text\": \"this is a test of the \\\\\"safe-code-safe-code-safe-code\\\\\" approach. The method only accepts two parameters as parameters: the code. The parameter \\'unsafe-code-safe-code-safe-code\\' should\"}']}]}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"args\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"this is a test\"],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" + ] + }, + { + "cell_type": "markdown", + "id": "ad88a78f", + "metadata": {}, + "source": [ + "## Testing Supported Tasks\n", + "\n", + "We can support multiple other transformers other than just text generation, below includes examples for a few other tasks supported.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e9ee06c8", + "metadata": {}, + "source": [ + "### Question Answering" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4492dc01", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"question-answering\"\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "9abf39e0", + "metadata": {}, + "source": [ + "Once again, you are able to run the model using the MLServer CLI.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d7aaf365", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '4efac938-86d8-41a1-b78f-7690b2dcf197',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"score\": 0.9869915843009949, \"start\": 12, \"end\": 18, \"answer\": \"Seldon\"}']}]}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"question\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"what is your name?\"],\n", + " },\n", + " {\n", + " \"name\": \"context\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"Hello, I am Seldon, how is it going\"],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" + ] + }, + { + "cell_type": "markdown", + "id": "5aa51356", + "metadata": {}, + "source": [ + "### Sentiment Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8e70c7d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-classification\"\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "ed336525", + "metadata": {}, + "source": [ + "Once again, you are able to run the model using the MLServer CLI.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2f704413", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_name': 'transformer',\n", + " 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b',\n", + " 'parameters': {},\n", + " 'outputs': [{'name': 'output',\n", + " 'shape': [1, 1],\n", + " 'datatype': 'BYTES',\n", + " 'parameters': {'content_type': 'hg_jsonlist'},\n", + " 'data': ['{\"label\": \"NEGATIVE\", \"score\": 0.9996137022972107}']}]}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"args\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"This is terrible!\"],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" + ] + }, + { + "cell_type": "markdown", + "id": "f1d4b24a-4c09-4743-a086-6f8b143711ad", + "metadata": {}, + "source": [ + "### Masked Language Modeling\n", + "\n", + "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "63e368ee-a5ef-44b7-aab8-cafd30ab227a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"fill-mask\",\n", + " \"pretrained_model\": \"cl-tohoku/bert-base-japanese\",\n", + " \"pretrained_tokenizer\": \"cl-tohoku/bert-base-japanese\"\n", + " }\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "72b05244-f52f-41c0-a1bc-a5d11d1c75a0", + "metadata": {}, + "source": [ + "Using the shell to start mlserver like so,\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```\n", + "we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b619c852-f35d-4506-91e1-0a1e3bcb7b8b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'実際 に 空 が 見える の か?'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlserver_huggingface.codecs import HuggingfaceRequestCodec\n", + "import json\n", + "\n", + "# Test sentence: Is the sky really [MASK]?\n", + "test_sentence = \"実際に空が[MASK]のか？\"\n", + "# [MASK] = visible\n", + "expected_output = \"見える\"\n", + "\n", + "inference_request = HuggingfaceRequestCodec.encode_request(\n", + " {\"inputs\": [test_sentence]},\n", + " use_bytes=False,\n", + ")\n", + "json.dumps(inference_request.dict())\n", + "response = requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request.dict()).json()\n", + "json.loads(response['outputs'][0]['data'][0])[\"sequence\"]" + ] + }, + { + "cell_type": "markdown", + "id": "fe6655d9", + "metadata": {}, + "source": [ + "## GPU Acceleration\n", + "\n", + "We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters\n", + "\n", + "### Testing with CPU\n", + "\n", + "We first test the time taken with the device=-1 which configures CPU by default" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "827472eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"max_batch_size\": 128,\n", + " \"max_batch_time\": 1,\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"device\": -1\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "eceb4d12", + "metadata": {}, + "source": [ + "Once again, you are able to run the model using the MLServer CLI.\n", + "\n", + "```shell\n", + "mlserver start .\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "888501c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time: 66.42268538899953\n" + ] + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"text_inputs\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"This is a generation for the work\" for i in range(512)],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "# Benchmark time\n", + "import time\n", + "start_time = time.monotonic()\n", + "\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request)\n", + "\n", + "print(f\"Elapsed time: {time.monotonic() - start_time}\")" + ] + }, + { + "cell_type": "markdown", + "id": "92756662", + "metadata": {}, + "source": [ + "We can see that it takes 81 seconds which is 8 times longer than the gpu example below." + ] + }, + { + "cell_type": "markdown", + "id": "cd2e208c", + "metadata": {}, + "source": [ + "### Testing with GPU\n", + "\n", + "IMPORTANT: Running the code below requries having a machine with GPU configured correctly to work for Tensorflow/Pytorch.\n", + " \n", + "Now we'll run the benchmark with GPU configured, which we can do by setting `device=0`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "032b8f4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"device\": 0\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1b6bd339", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time: 11.27933280000434\n" + ] + } + ], + "source": [ + "inference_request = {\n", + " \"inputs\": [\n", + " {\n", + " \"name\": \"text_inputs\",\n", + " \"shape\": [1],\n", + " \"datatype\": \"BYTES\",\n", + " \"data\": [\"This is a generation for the work\" for i in range(512)],\n", + " }\n", + " ]\n", + "}\n", + "\n", + "# Benchmark time\n", + "import time\n", + "start_time = time.monotonic()\n", + "\n", + "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request)\n", + "\n", + "print(f\"Elapsed time: {time.monotonic() - start_time}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c929108c", + "metadata": {}, + "source": [ + "We can see that the elapsed time is 8 times less than the CPU version!" + ] + }, + { + "cell_type": "markdown", + "id": "5d790b37", + "metadata": {}, + "source": [ + "### Adaptive Batching with GPU\n", + "\n", + "We can also see how the adaptive batching capabilities can allow for GPU acceleration by grouping multiple incoming requests so they get processed in GPU batch.\n", + "\n", + "In our case we can enable adaptive batching with the `max_batch_size` which in our case we will set it ot 128.\n", + "\n", + "We will also configure `max_batch_time` which specifies` the maximum amount of time the MLServer orchestrator will wait before sending for inference." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "810a4abe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting ./model-settings.json\n" + ] + } + ], + "source": [ + "%%writefile ./model-settings.json\n", + "{\n", + " \"name\": \"transformer\",\n", + " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", + " \"max_batch_size\": 128,\n", + " \"max_batch_time\": 1,\n", + " \"parameters\": {\n", + " \"extra\": {\n", + " \"task\": \"text-generation\",\n", + " \"pretrained_model\": \"distilgpt2\",\n", + " \"device\": 0\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "e063ccb6", + "metadata": {}, + "source": [ + "In order to achieve the throughput required of 50 requests per second, we will use the tool `vegeta` which performs load testing.\n", + "\n", + "We can now see that we are able to see that the requests are batched and we receive 100% success eventhough the requests are sent one-by-one." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1a787909", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requests [total, rate, throughput] 150, 50.34, 22.28\n", + "Duration [total, attack, wait] 6.732s, 2.98s, 3.753s\n", + "Latencies [min, mean, 50, 90, 95, 99, max] 1.975s, 3.168s, 3.22s, 4.065s, 4.183s, 4.299s, 4.318s\n", + "Bytes In [total, mean] 60978, 406.52\n", + "Bytes Out [total, mean] 12300, 82.00\n", + "Success [ratio] 100.00%\n", + "Status Codes [code:count] 200:150 \n", + "Error Set:\n" + ] + } + ], + "source": [ + "%%bash\n", + "jq -ncM '{\"method\": \"POST\", \"header\": {\"Content-Type\": [\"application/json\"] }, \"url\": \"http://localhost:8080/v2/models/transformer/infer\", \"body\": \"{\\\"inputs\\\":[{\\\"name\\\":\\\"text_inputs\\\",\\\"shape\\\":[1],\\\"datatype\\\":\\\"BYTES\\\",\\\"data\\\":[\\\"test\\\"]}]}\" | @base64 }' \\\n", + " | vegeta \\\n", + " -cpus=\"2\" \\\n", + " attack \\\n", + " -duration=\"3s\" \\\n", + " -rate=\"50\" \\\n", + " -format=json \\\n", + " | vegeta \\\n", + " report \\\n", + " -type=text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ddcb458", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/huggingface/README.md b/docs/examples/huggingface/README.md index 3e3b5c25a..6c7603cfb 100644 --- a/docs/examples/huggingface/README.md +++ b/docs/examples/huggingface/README.md @@ -276,7 +276,11 @@ requests.post( ### Masked Language Modeling -We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers. +We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers. For example, after running the normal project build from the root directory with `make install-dev`, we can install the optional Japanese dependencies in dev mode: + +`poetry install -E japanese` + +from the `./runtimes/huggingface` level. ```python %%writefile ./model-settings.json diff --git a/runtimes/huggingface/poetry.lock b/runtimes/huggingface/poetry.lock index 38c05a1e3..f0b7d2600 100644 --- a/runtimes/huggingface/poetry.lock +++ b/runtimes/huggingface/poetry.lock @@ -185,13 +185,22 @@ frozenlist = ">=1.1.0" [[package]] name = "anyio" +<<<<<<< HEAD version = "4.3.0" +======= +version = "4.2.0" +>>>>>>> Updated lock and docs description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ +<<<<<<< HEAD {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"}, {file = "anyio-4.3.0.tar.gz", hash = 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827329f7e0e3198dbf122c25ea288037032ecef9 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 16 Jan 2024 10:07:21 -0800 Subject: [PATCH 42/49] Removed .bak.ipynb --- docs/examples/huggingface/README.bak.ipynb | 815 --------------------- 1 file changed, 815 deletions(-) delete mode 100755 docs/examples/huggingface/README.bak.ipynb diff --git a/docs/examples/huggingface/README.bak.ipynb b/docs/examples/huggingface/README.bak.ipynb deleted file mode 100755 index b7e69b60b..000000000 --- a/docs/examples/huggingface/README.bak.ipynb +++ /dev/null @@ -1,815 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8c519626", - "metadata": {}, - "source": [ - "# Serving HuggingFace Transformer Models\n", - "\n", - "Out of the box, MLServer supports the deployment and serving of HuggingFace Transformer models with the following features:\n", - "\n", - "- Loading of Transformer Model artifacts from the Hugging Face Hub.\n", - "- Model quantization & optimization using the Hugging Face Optimum library\n", - "- Request batching for GPU optimization (via adaptive batching and request batching)\n", - "\n", - "In this example, we will showcase some of this features using an example model." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b5b2588c", - "metadata": {}, - "outputs": [], - "source": [ - "# Import required dependencies\n", - "import requests" - ] - }, - { - "cell_type": "markdown", - "id": "f1fed35e", - "metadata": {}, - "source": [ - "## Serving\n", - "\n", - "Now that we have trained and serialised our model, we are ready to start serving it.\n", - "For that, the initial step will be to set up a `model-settings.json` that instructs MLServer to load our artifact using the HuggingFace Inference Runtime.\n", - "\n", - "We will show how to add share a task " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6df62443", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"text-generation\",\n", - " \"pretrained_model\": \"distilgpt2\"\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "c6a6e8b2", - "metadata": {}, - "source": [ - "Now that we have our config in-place, we can start the server by running `mlserver start .`. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```\n", - "\n", - "Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal." - ] - }, - { - "cell_type": "markdown", - "id": "b664c591", - "metadata": {}, - "source": [ - "### Send test inference request\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "759ad7df", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': 'eb160c6b-8223-4342-ad92-6ac301a9fa5d',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"generated_text\": \"this is a testnet with 1-3,000-bit nodes as nodes.\"}']}]}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"args\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"this is a test\"],\n", - " }\n", - " ]\n", - "}\n", - "\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" - ] - }, - { - "cell_type": "markdown", - "id": "0edbcf72", - "metadata": {}, - "source": [ - "### Using Optimum Optimized Models\n", - "\n", - "We can also leverage the Optimum library that allows us to access quantized and optimized models. \n", - "\n", - "We can download pretrained optimized models from the hub if available by enabling the `optimum_model` flag:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6d185281", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"text-generation\",\n", - " \"pretrained_model\": \"distilgpt2\",\n", - " \"optimum_model\": true\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "90925ef0", - "metadata": {}, - "source": [ - "Once again, you are able to run the model using the MLServer CLI. As before this needs to either be ran from the same directory where our config files are or pointing to the folder where they are.\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "6945db96", - "metadata": {}, - "source": [ - "### Send Test Request to Optimum Optimized Model\n", - "\n", - "The request can now be sent using the same request structure but using optimized models for better performance." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "39d8b438", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': '9c482c8d-b21e-44b1-8a42-7650a9dc01ef',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"generated_text\": \"this is a test of the \\\\\"safe-code-safe-code-safe-code\\\\\" approach. The method only accepts two parameters as parameters: the code. The parameter \\'unsafe-code-safe-code-safe-code\\' should\"}']}]}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"args\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"this is a test\"],\n", - " }\n", - " ]\n", - "}\n", - "\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" - ] - }, - { - "cell_type": "markdown", - "id": "ad88a78f", - "metadata": {}, - "source": [ - "## Testing Supported Tasks\n", - "\n", - "We can support multiple other transformers other than just text generation, below includes examples for a few other tasks supported.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "e9ee06c8", - "metadata": {}, - "source": [ - "### Question Answering" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4492dc01", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"question-answering\"\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "9abf39e0", - "metadata": {}, - "source": [ - "Once again, you are able to run the model using the MLServer CLI.\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d7aaf365", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': '4efac938-86d8-41a1-b78f-7690b2dcf197',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"score\": 0.9869915843009949, \"start\": 12, \"end\": 18, \"answer\": \"Seldon\"}']}]}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"question\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"what is your name?\"],\n", - " },\n", - " {\n", - " \"name\": \"context\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"Hello, I am Seldon, how is it going\"],\n", - " }\n", - " ]\n", - "}\n", - "\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" - ] - }, - { - "cell_type": "markdown", - "id": "5aa51356", - "metadata": {}, - "source": [ - "### Sentiment Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "8e70c7d7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"text-classification\"\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "ed336525", - "metadata": {}, - "source": [ - "Once again, you are able to run the model using the MLServer CLI.\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2f704413", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'model_name': 'transformer',\n", - " 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b',\n", - " 'parameters': {},\n", - " 'outputs': [{'name': 'output',\n", - " 'shape': [1, 1],\n", - " 'datatype': 'BYTES',\n", - " 'parameters': {'content_type': 'hg_jsonlist'},\n", - " 'data': ['{\"label\": \"NEGATIVE\", \"score\": 0.9996137022972107}']}]}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"args\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"This is terrible!\"],\n", - " }\n", - " ]\n", - "}\n", - "\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request).json()" - ] - }, - { - "cell_type": "markdown", - "id": "f1d4b24a-4c09-4743-a086-6f8b143711ad", - "metadata": {}, - "source": [ - "### Masked Language Modeling\n", - "\n", - "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "63e368ee-a5ef-44b7-aab8-cafd30ab227a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.runtime.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"fill-mask\",\n", - " \"pretrained_model\": \"cl-tohoku/bert-base-japanese\",\n", - " \"pretrained_tokenizer\": \"cl-tohoku/bert-base-japanese\"\n", - " }\n", - " }" - ] - }, - { - "cell_type": "markdown", - "id": "72b05244-f52f-41c0-a1bc-a5d11d1c75a0", - "metadata": {}, - "source": [ - "Using the shell to start mlserver like so,\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```\n", - "we can pass inferences like this. Note the `[MASK]` token. The mask token can be different for different models, so check the HuggingFace model config for special tokens." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b619c852-f35d-4506-91e1-0a1e3bcb7b8b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'実際 に 空 が 見える の か?'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlserver_huggingface.codecs import HuggingfaceRequestCodec\n", - "import json\n", - "\n", - "# Test sentence: Is the sky really [MASK]?\n", - "test_sentence = \"実際に空が[MASK]のか？\"\n", - "# [MASK] = visible\n", - "expected_output = \"見える\"\n", - "\n", - "inference_request = HuggingfaceRequestCodec.encode_request(\n", - " {\"inputs\": [test_sentence]},\n", - " use_bytes=False,\n", - ")\n", - "json.dumps(inference_request.dict())\n", - "response = requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request.dict()).json()\n", - "json.loads(response['outputs'][0]['data'][0])[\"sequence\"]" - ] - }, - { - "cell_type": "markdown", - "id": "fe6655d9", - "metadata": {}, - "source": [ - "## GPU Acceleration\n", - "\n", - "We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters\n", - "\n", - "### Testing with CPU\n", - "\n", - "We first test the time taken with the device=-1 which configures CPU by default" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "827472eb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"max_batch_size\": 128,\n", - " \"max_batch_time\": 1,\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"text-generation\",\n", - " \"device\": -1\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "eceb4d12", - "metadata": {}, - "source": [ - "Once again, you are able to run the model using the MLServer CLI.\n", - "\n", - "```shell\n", - "mlserver start .\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "888501c1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time: 66.42268538899953\n" - ] - } - ], - "source": [ - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"text_inputs\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"This is a generation for the work\" for i in range(512)],\n", - " }\n", - " ]\n", - "}\n", - "\n", - "# Benchmark time\n", - "import time\n", - "start_time = time.monotonic()\n", - "\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request)\n", - "\n", - "print(f\"Elapsed time: {time.monotonic() - start_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "92756662", - "metadata": {}, - "source": [ - "We can see that it takes 81 seconds which is 8 times longer than the gpu example below." - ] - }, - { - "cell_type": "markdown", - "id": "cd2e208c", - "metadata": {}, - "source": [ - "### Testing with GPU\n", - "\n", - "IMPORTANT: Running the code below requries having a machine with GPU configured correctly to work for Tensorflow/Pytorch.\n", - " \n", - "Now we'll run the benchmark with GPU configured, which we can do by setting `device=0`" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "032b8f4e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"text-generation\",\n", - " \"device\": 0\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "1b6bd339", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time: 11.27933280000434\n" - ] - } - ], - "source": [ - "inference_request = {\n", - " \"inputs\": [\n", - " {\n", - " \"name\": \"text_inputs\",\n", - " \"shape\": [1],\n", - " \"datatype\": \"BYTES\",\n", - " \"data\": [\"This is a generation for the work\" for i in range(512)],\n", - " }\n", - " ]\n", - "}\n", - "\n", - "# Benchmark time\n", - "import time\n", - "start_time = time.monotonic()\n", - "\n", - "requests.post(\"http://localhost:8080/v2/models/transformer/infer\", json=inference_request)\n", - "\n", - "print(f\"Elapsed time: {time.monotonic() - start_time}\")" - ] - }, - { - "cell_type": "markdown", - "id": "c929108c", - "metadata": {}, - "source": [ - "We can see that the elapsed time is 8 times less than the CPU version!" - ] - }, - { - "cell_type": "markdown", - "id": "5d790b37", - "metadata": {}, - "source": [ - "### Adaptive Batching with GPU\n", - "\n", - "We can also see how the adaptive batching capabilities can allow for GPU acceleration by grouping multiple incoming requests so they get processed in GPU batch.\n", - "\n", - "In our case we can enable adaptive batching with the `max_batch_size` which in our case we will set it ot 128.\n", - "\n", - "We will also configure `max_batch_time` which specifies` the maximum amount of time the MLServer orchestrator will wait before sending for inference." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "810a4abe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting ./model-settings.json\n" - ] - } - ], - "source": [ - "%%writefile ./model-settings.json\n", - "{\n", - " \"name\": \"transformer\",\n", - " \"implementation\": \"mlserver_huggingface.HuggingFaceRuntime\",\n", - " \"max_batch_size\": 128,\n", - " \"max_batch_time\": 1,\n", - " \"parameters\": {\n", - " \"extra\": {\n", - " \"task\": \"text-generation\",\n", - " \"pretrained_model\": \"distilgpt2\",\n", - " \"device\": 0\n", - " }\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "e063ccb6", - "metadata": {}, - "source": [ - "In order to achieve the throughput required of 50 requests per second, we will use the tool `vegeta` which performs load testing.\n", - "\n", - "We can now see that we are able to see that the requests are batched and we receive 100% success eventhough the requests are sent one-by-one." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "1a787909", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requests [total, rate, throughput] 150, 50.34, 22.28\n", - "Duration [total, attack, wait] 6.732s, 2.98s, 3.753s\n", - "Latencies [min, mean, 50, 90, 95, 99, max] 1.975s, 3.168s, 3.22s, 4.065s, 4.183s, 4.299s, 4.318s\n", - "Bytes In [total, mean] 60978, 406.52\n", - "Bytes Out [total, mean] 12300, 82.00\n", - "Success [ratio] 100.00%\n", - "Status Codes [code:count] 200:150 \n", - "Error Set:\n" - ] - } - ], - "source": [ - "%%bash\n", - "jq -ncM '{\"method\": \"POST\", \"header\": {\"Content-Type\": [\"application/json\"] }, \"url\": \"http://localhost:8080/v2/models/transformer/infer\", \"body\": \"{\\\"inputs\\\":[{\\\"name\\\":\\\"text_inputs\\\",\\\"shape\\\":[1],\\\"datatype\\\":\\\"BYTES\\\",\\\"data\\\":[\\\"test\\\"]}]}\" | @base64 }' \\\n", - " | vegeta \\\n", - " -cpus=\"2\" \\\n", - " attack \\\n", - " -duration=\"3s\" \\\n", - " -rate=\"50\" \\\n", - " -format=json \\\n", - " | vegeta \\\n", - " report \\\n", - " -type=text" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0ddcb458", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From c9372a56f5be13768cb8ab9873922079f7b333d9 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 16 Jan 2024 10:09:09 -0800 Subject: [PATCH 43/49] Updated README.ipynb to match README.md --- docs/examples/huggingface/README.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/examples/huggingface/README.ipynb b/docs/examples/huggingface/README.ipynb index 9922bd637..3e29d9f4f 100644 --- a/docs/examples/huggingface/README.ipynb +++ b/docs/examples/huggingface/README.ipynb @@ -439,13 +439,14 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "f1d4b24a-4c09-4743-a086-6f8b143711ad", "metadata": {}, "source": [ - "### Masked Language Modeling\n", + "### Masked Language Modeling (Optional Japanese Language Example)\n", "\n", - "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers." + "We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers. For example, after running the normal project build from the root directory with `make install-dev`, we can install the optional Japanese dependencies in dev mode:" ] }, { From e137c05a9bcd3dd4731dd584952174217dcfd404 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Fri, 12 Apr 2024 08:04:43 -0700 Subject: [PATCH 44/49] rebuilt lock files with same version of poetry --- runtimes/alibi-detect/poetry.lock | 1173 ++++++++++++----------- runtimes/alibi-explain/poetry.lock | 1402 ++++++++++++++-------------- runtimes/catboost/poetry.lock | 1032 ++++++++++---------- runtimes/lightgbm/poetry.lock | 624 ++++++------- runtimes/mlflow/poetry.lock | 1262 +++++++++++++------------ runtimes/mllib/poetry.lock | 626 ++++++------- runtimes/sklearn/poetry.lock | 692 +++++++------- runtimes/xgboost/poetry.lock | 746 ++++++++------- 8 files changed, 3763 insertions(+), 3794 deletions(-) diff --git a/runtimes/alibi-detect/poetry.lock b/runtimes/alibi-detect/poetry.lock index 79c289be8..33c6a9608 100644 --- 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"pytest-ruff"] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [[package]] name = "zope-event" From d9211bf9f4377c01c7821c9f4a6a7092f25e6ceb Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 16 Apr 2024 10:59:53 -0400 Subject: [PATCH 45/49] Fixed merge conflict in README --- docs/examples/huggingface/README.md | 29 ++++++++++++++--------------- 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/docs/examples/huggingface/README.md b/docs/examples/huggingface/README.md index 6c7603cfb..6d5ecca12 100644 --- a/docs/examples/huggingface/README.md +++ b/docs/examples/huggingface/README.md @@ -259,22 +259,18 @@ requests.post( "http://localhost:8080/v2/models/transformer/infer", json=inference_request ).json() ``` +``` +{'model_name': 'transformer', + 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b', + 'parameters': {}, + 'outputs': [{'name': 'output', + 'shape': [1, 1], + 'datatype': 'BYTES', + 'parameters': {'content_type': 'hg_jsonlist'}, + 'data': ['{"label": "NEGATIVE", "score": 0.9996137022972107}']}]} +``` - - - - {'model_name': 'transformer', - 'id': '835eabbd-daeb-4423-a64f-a7c4d7c60a9b', - 'parameters': {}, - 'outputs': [{'name': 'output', - 'shape': [1, 1], - 'datatype': 'BYTES', - 'parameters': {'content_type': 'hg_jsonlist'}, - 'data': ['{"label": "NEGATIVE", "score": 0.9996137022972107}']}]} - - - -### Masked Language Modeling +### Masked Language Modeling (Optional Japanese Language Example) We can also serve a masked language model. In the following example, we also build the `huggingface` runtime with the `-E japanese` flag to enable support for Japanese tokenizers. For example, after running the normal project build from the root directory with `make install-dev`, we can install the optional Japanese dependencies in dev mode: @@ -318,12 +314,15 @@ json.dumps(inference_request.dict()) response = requests.post("http://localhost:8080/v2/models/transformer/infer", json=inference_request.dict()).json() json.loads(response['outputs'][0]['data'][0])["sequence"] ``` +<<<<<<< HEAD ``` Response: {'model_name': 'transformer', 'id': '9e966d8d-b43d-4ab4-8d47-90e367196233', 'parameters': {}, 'outputs': [{'name': 'output', 'shape': [5, 1], 'datatype': 'BYTES', 'parameters': {'content_type': 'hg_jsonlist'}, 'data': ['{"score": 0.3277095854282379, "token": 11819, "token_str": "\\u3042\\u308b", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u3042\\u308b \\u306e \\u304b?"}', '{"score": 0.10271108895540237, "token": 14656, "token_str": "\\u898b\\u3048\\u308b", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u898b\\u3048\\u308b \\u306e \\u304b?"}', '{"score": 0.08325661718845367, "token": 11835, "token_str": "\\u306a\\u3044", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u306a\\u3044 \\u306e \\u304b?"}', '{"score": 0.036131054162979126, "token": 18413, "token_str": "\\u6b63\\u3057\\u3044", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u6b63\\u3057\\u3044 \\u306e \\u304b?"}', '{"score": 0.029351236298680305, "token": 11820, "token_str": "\\u3044\\u308b", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u3044\\u308b \\u306e \\u304b?"}']}]} Data: {'score': 0.3277095854282379, 'token': 11819, 'token_str': 'ある', 'sequence': '実際 に 空 が ある の か?'} ``` +======= +>>>>>>> Fixed merge conflict in README ## GPU Acceleration We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters From 4a9c1c6da9f44f78eab1879c0a98109a6f738fd6 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 16 Apr 2024 11:07:30 -0400 Subject: [PATCH 46/49] Fixed merge conflict in lock --- runtimes/huggingface/poetry.lock | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/runtimes/huggingface/poetry.lock b/runtimes/huggingface/poetry.lock index f0b7d2600..cf0b92603 100644 --- a/runtimes/huggingface/poetry.lock +++ b/runtimes/huggingface/poetry.lock @@ -2441,14 +2441,19 @@ tests = ["pytest", "pytest-cov", "pytest-pep8"] [[package]] name = "optimum" <<<<<<< HEAD +<<<<<<< HEAD version = "1.19.0" ======= version = "1.16.1" >>>>>>> Updated lock and docs +======= +version = "1.19.0" +>>>>>>> Fixed merge conflict in lock description = "Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality." optional = false python-versions = ">=3.7.0" files = [ +<<<<<<< HEAD <<<<<<< HEAD {file = "optimum-1.19.0-py3-none-any.whl", hash = "sha256:b259b2379f6904d7e1bef3f9ab1c3f22ae6c42357b416311950cd65526bb485e"}, {file = "optimum-1.19.0.tar.gz", hash = "sha256:a1eb134d70d3093eca68160fcf84c1aff8887726641f3362adca878fda095e3d"}, @@ -2456,6 +2461,10 @@ files = [ {file = "optimum-1.16.1-py3-none-any.whl", hash = "sha256:c0d0cb5877ae7b8f40289c54885341297f61b30255a93a71f5e979bc86b03598"}, {file = "optimum-1.16.1.tar.gz", hash = "sha256:9a74bc5cbbe2373fc45f477257d5046fd28b725bce91c1f8846baadba57f2129"}, >>>>>>> Updated lock and docs +======= + {file = "optimum-1.19.0-py3-none-any.whl", hash = "sha256:b259b2379f6904d7e1bef3f9ab1c3f22ae6c42357b416311950cd65526bb485e"}, + {file = "optimum-1.19.0.tar.gz", hash = "sha256:a1eb134d70d3093eca68160fcf84c1aff8887726641f3362adca878fda095e3d"}, +>>>>>>> Fixed merge conflict in lock ] [package.dependencies] @@ -3715,14 +3724,19 @@ files = [ [[package]] name = "setuptools" <<<<<<< HEAD +<<<<<<< HEAD version = "69.5.1" ======= version = "69.0.3" >>>>>>> Updated lock and docs +======= +version = "69.5.1" +>>>>>>> Fixed merge conflict in lock description = "Easily download, build, install, upgrade, and uninstall Python packages" optional = false python-versions = ">=3.8" files = [ +<<<<<<< HEAD <<<<<<< HEAD {file = "setuptools-69.5.1-py3-none-any.whl", hash = "sha256:c636ac361bc47580504644275c9ad802c50415c7522212252c033bd15f301f32"}, {file = "setuptools-69.5.1.tar.gz", hash = "sha256:6c1fccdac05a97e598fb0ae3bbed5904ccb317337a51139dcd51453611bbb987"}, @@ -3730,6 +3744,10 @@ files = [ {file = "setuptools-69.0.3-py3-none-any.whl", hash = "sha256:385eb4edd9c9d5c17540511303e39a147ce2fc04bc55289c322b9e5904fe2c05"}, {file = "setuptools-69.0.3.tar.gz", hash = "sha256:be1af57fc409f93647f2e8e4573a142ed38724b8cdd389706a867bb4efcf1e78"}, >>>>>>> Updated lock and docs +======= + {file = "setuptools-69.5.1-py3-none-any.whl", hash = "sha256:c636ac361bc47580504644275c9ad802c50415c7522212252c033bd15f301f32"}, + {file = "setuptools-69.5.1.tar.gz", hash = "sha256:6c1fccdac05a97e598fb0ae3bbed5904ccb317337a51139dcd51453611bbb987"}, +>>>>>>> Fixed merge conflict in lock ] [package.extras] From 85bddf185600179e29862506dd3e5cb3631476cf Mon Sep 17 00:00:00 2001 From: jbauer2718 <51673035+jbauer2718@users.noreply.github.com> Date: Tue, 16 Apr 2024 12:30:08 -0400 Subject: [PATCH 47/49] Update README.md --- docs/examples/huggingface/README.md | 6 ------ 1 file changed, 6 deletions(-) diff --git a/docs/examples/huggingface/README.md b/docs/examples/huggingface/README.md index 25af80719..caf5dfda0 100644 --- a/docs/examples/huggingface/README.md +++ b/docs/examples/huggingface/README.md @@ -314,18 +314,12 @@ json.dumps(inference_request.dict()) response = requests.post("http://localhost:8080/v2/models/transformer/infer", json=inference_request.dict()).json() json.loads(response['outputs'][0]['data'][0])["sequence"] ``` -<<<<<<< HEAD -<<<<<<< HEAD ``` Response: {'model_name': 'transformer', 'id': '9e966d8d-b43d-4ab4-8d47-90e367196233', 'parameters': {}, 'outputs': [{'name': 'output', 'shape': [5, 1], 'datatype': 'BYTES', 'parameters': {'content_type': 'hg_jsonlist'}, 'data': ['{"score": 0.3277095854282379, "token": 11819, "token_str": "\\u3042\\u308b", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u3042\\u308b \\u306e \\u304b?"}', '{"score": 0.10271108895540237, "token": 14656, "token_str": "\\u898b\\u3048\\u308b", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u898b\\u3048\\u308b \\u306e \\u304b?"}', '{"score": 0.08325661718845367, "token": 11835, "token_str": "\\u306a\\u3044", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u306a\\u3044 \\u306e \\u304b?"}', '{"score": 0.036131054162979126, "token": 18413, "token_str": "\\u6b63\\u3057\\u3044", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u6b63\\u3057\\u3044 \\u306e \\u304b?"}', '{"score": 0.029351236298680305, "token": 11820, "token_str": "\\u3044\\u308b", "sequence": "\\u5b9f\\u969b \\u306b \\u7a7a \\u304c \\u3044\\u308b \\u306e \\u304b?"}']}]} Data: {'score': 0.3277095854282379, 'token': 11819, 'token_str': 'ある', 'sequence': '実際 に 空 が ある の か?'} ``` -======= ->>>>>>> Fixed merge conflict in README -======= ->>>>>>> 7f21db5f34f1cef2b6c42ac17b979f02d15f58d9 ## GPU Acceleration We can also evaluate GPU acceleration, we can test the speed on CPU vs GPU using the following parameters From 616d3a98e91ada6e313f58a55537d0f2a10d88da Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 16 Apr 2024 13:52:41 -0400 Subject: [PATCH 48/49] removed files for bert-japanese --- bert-japanese-small/model-settings.json | 25 ------------------------- bert-japanese/model-settings.json | 11 ----------- 2 files changed, 36 deletions(-) delete mode 100644 bert-japanese-small/model-settings.json delete mode 100644 bert-japanese/model-settings.json diff --git a/bert-japanese-small/model-settings.json b/bert-japanese-small/model-settings.json deleted file mode 100644 index 7b6e22801..000000000 --- a/bert-japanese-small/model-settings.json +++ /dev/null @@ -1,25 +0,0 @@ -<<<<<<< HEAD -{ - "name": "transformer", - "implementation": "mlserver_huggingface.runtime.HuggingFaceRuntime", - "parameters": { - "extra": { - "task": "fill-mask", - "pretrained_model": "izumi-lab/bert-small-japanese", - "pretrained_tokenizer": "izumi-lab/bert-small-japanese" - } - } - } -======= -{ - "name": "transformer", - "implementation": "mlserver_huggingface.runtime.HuggingFaceRuntime", - "parameters": { - "extra": { - "task": "fill-mask", - "pretrained_model": "izumi-lab/bert-small-japanese", - "pretrained_tokenizer": "izumi-lab/bert-small-japanese" - } - } -} ->>>>>>> 7f21db5f34f1cef2b6c42ac17b979f02d15f58d9 diff --git a/bert-japanese/model-settings.json b/bert-japanese/model-settings.json deleted file mode 100644 index 1fe168921..000000000 --- a/bert-japanese/model-settings.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "name": "transformer", - "implementation": "mlserver_huggingface.runtime.HuggingFaceRuntime", - "parameters": { - "extra": { - "task": "fill-mask", - "pretrained_model": "cl-tohoku/bert-base-japanese", - "pretrained_tokenizer": "cl-tohoku/bert-base-japanese" - } - } -} \ No newline at end of file From cf4ec150107842c92ca51dc091a31dbe4f486996 Mon Sep 17 00:00:00 2001 From: Jake Bauer Date: Tue, 16 Apr 2024 13:53:54 -0400 Subject: [PATCH 49/49] Removed model setting --- docs/examples/huggingface/model-settings.json | 18 ------------------ 1 file changed, 18 deletions(-) delete mode 100644 docs/examples/huggingface/model-settings.json diff --git a/docs/examples/huggingface/model-settings.json b/docs/examples/huggingface/model-settings.json deleted file mode 100644 index 56644b454..000000000 --- a/docs/examples/huggingface/model-settings.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "name": "transformer", - "implementation": "mlserver_huggingface.runtime.HuggingFaceRuntime", - "parameters": { - "extra": { - "task": "fill-mask", -<<<<<<< HEAD - "pretrained_model": "izumi-lab/bert-small-japanese", - "pretrained_tokenizer": "izumi-lab/bert-small-japanese" - } - } -} -======= - "pretrained_model": "cl-tohoku/bert-base-japanese", - "pretrained_tokenizer": "cl-tohoku/bert-base-japanese" - } - } ->>>>>>> 7f21db5f34f1cef2b6c42ac17b979f02d15f58d9