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Fix: ModelBuilder deployment & optimization of JumpStart llama-3.1 mo…
…dels (#4937) * Emit warning when cpu cores are requested with sharded model deployment. * Reformat sharded model validations. * fix pop on none error in jumpstart draft model flow * set lmi config on js model optimize * re-format lmi config switch * add e2e UT for lmi + .optimize() * add e2e UT for lmi + .optimize() no override * add deep UTs to catch regressions and test E2E fully and more practically * work around flake8 bug * flake8 workaround * fix flake8 syntax error in py38 --------- Co-authored-by: Joseph Zhang <[email protected]> Co-authored-by: Gary Wang 😤 <[email protected]>
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238 changes: 238 additions & 0 deletions
238
tests/integ/sagemaker/serve/test_serve_js_deep_unit_tests.py
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| # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"). You | ||
| # may not use this file except in compliance with the License. A copy of | ||
| # the License is located at | ||
| # | ||
| # http://aws.amazon.com/apache2.0/ | ||
| # | ||
| # or in the "license" file accompanying this file. This file is | ||
| # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF | ||
| # ANY KIND, either express or implied. See the License for the specific | ||
| # language governing permissions and limitations under the License. | ||
| from __future__ import absolute_import | ||
| from unittest.mock import MagicMock, patch, ANY | ||
|
|
||
| from sagemaker.session import Session | ||
| from sagemaker.serve.builder.model_builder import ModelBuilder | ||
| from sagemaker.serve.builder.schema_builder import SchemaBuilder | ||
| from sagemaker.resource_requirements import ResourceRequirements | ||
|
|
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| ROLE_NAME = "SageMakerRole" | ||
|
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|
|
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| def test_js_model_with_optimize_speculative_decoding_config_gated_requests_are_expected( | ||
| sagemaker_session, | ||
| ): | ||
| with patch.object( | ||
| Session, "create_model", return_value="mock_model" | ||
| ) as mock_create_model, patch.object( | ||
| Session, "endpoint_from_production_variants" | ||
| ) as mock_endpoint_from_production_variants: | ||
| iam_client = sagemaker_session.boto_session.client("iam") | ||
| role_arn = iam_client.get_role(RoleName=ROLE_NAME)["Role"]["Arn"] | ||
|
|
||
| schema_builder = SchemaBuilder("test", "test") | ||
| model_builder = ModelBuilder( | ||
| model="meta-textgeneration-llama-3-1-8b-instruct", | ||
| schema_builder=schema_builder, | ||
| sagemaker_session=sagemaker_session, | ||
| role_arn=role_arn, | ||
| ) | ||
|
|
||
| optimized_model = model_builder.optimize( | ||
| instance_type="ml.g5.xlarge", # set to small instance in case a network call is made | ||
| speculative_decoding_config={ | ||
| "ModelProvider": "JumpStart", | ||
| "ModelID": "meta-textgeneration-llama-3-2-1b", | ||
| "AcceptEula": True, | ||
| }, | ||
| accept_eula=True, | ||
| ) | ||
|
|
||
| optimized_model.deploy() | ||
|
|
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| mock_create_model.assert_called_once_with( | ||
| name=ANY, | ||
| role=ANY, | ||
| container_defs={ | ||
| "Image": ANY, | ||
| "Environment": { | ||
| "SAGEMAKER_PROGRAM": "inference.py", | ||
| "ENDPOINT_SERVER_TIMEOUT": "3600", | ||
| "MODEL_CACHE_ROOT": "/opt/ml/model", | ||
| "SAGEMAKER_ENV": "1", | ||
| "HF_MODEL_ID": "/opt/ml/model", | ||
| "SAGEMAKER_MODEL_SERVER_WORKERS": "1", | ||
| "OPTION_SPECULATIVE_DRAFT_MODEL": "/opt/ml/additional-model-data-sources/draft_model/", | ||
| }, | ||
| "AdditionalModelDataSources": [ | ||
| { | ||
| "ChannelName": "draft_model", | ||
| "S3DataSource": { | ||
| "S3Uri": ANY, | ||
| "S3DataType": "S3Prefix", | ||
| "CompressionType": "None", | ||
| "ModelAccessConfig": {"AcceptEula": True}, | ||
| }, | ||
| } | ||
| ], | ||
| "ModelDataSource": { | ||
| "S3DataSource": { | ||
| "S3Uri": ANY, | ||
| "S3DataType": "S3Prefix", | ||
| "CompressionType": "None", | ||
| "ModelAccessConfig": {"AcceptEula": True}, | ||
| } | ||
| }, | ||
| }, | ||
| vpc_config=None, | ||
| enable_network_isolation=True, | ||
| tags=ANY, | ||
| ) | ||
| mock_endpoint_from_production_variants.assert_called_once() | ||
|
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||
|
|
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| def test_js_model_with_optimize_sharding_and_resource_requirements_requests_are_expected( | ||
| sagemaker_session, | ||
| ): | ||
| with patch.object( | ||
| Session, | ||
| "wait_for_optimization_job", | ||
| return_value={"OptimizationJobName": "mock_optimization_job"}, | ||
| ), patch.object( | ||
| Session, "create_model", return_value="mock_model" | ||
| ) as mock_create_model, patch.object( | ||
| Session, "endpoint_from_production_variants", return_value="mock_endpoint_name" | ||
| ) as mock_endpoint_from_production_variants, patch.object( | ||
| Session, "create_inference_component" | ||
| ) as mock_create_inference_component: | ||
| iam_client = sagemaker_session.boto_session.client("iam") | ||
| role_arn = iam_client.get_role(RoleName=ROLE_NAME)["Role"]["Arn"] | ||
|
|
||
| sagemaker_session.sagemaker_client.create_optimization_job = MagicMock() | ||
|
|
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| schema_builder = SchemaBuilder("test", "test") | ||
| model_builder = ModelBuilder( | ||
| model="meta-textgeneration-llama-3-1-8b-instruct", | ||
| schema_builder=schema_builder, | ||
| sagemaker_session=sagemaker_session, | ||
| role_arn=role_arn, | ||
| ) | ||
|
|
||
| optimized_model = model_builder.optimize( | ||
| instance_type="ml.g5.xlarge", # set to small instance in case a network call is made | ||
| sharding_config={"OverrideEnvironment": {"OPTION_TENSOR_PARALLEL_DEGREE": "8"}}, | ||
| accept_eula=True, | ||
| ) | ||
|
|
||
| optimized_model.deploy( | ||
| resources=ResourceRequirements(requests={"memory": 196608, "num_accelerators": 8}) | ||
| ) | ||
|
|
||
| mock_create_model.assert_called_once_with( | ||
| name=ANY, | ||
| role=ANY, | ||
| container_defs={ | ||
| "Image": ANY, | ||
| "Environment": { | ||
| "SAGEMAKER_PROGRAM": "inference.py", | ||
| "ENDPOINT_SERVER_TIMEOUT": "3600", | ||
| "MODEL_CACHE_ROOT": "/opt/ml/model", | ||
| "SAGEMAKER_ENV": "1", | ||
| "HF_MODEL_ID": "/opt/ml/model", | ||
| "SAGEMAKER_MODEL_SERVER_WORKERS": "1", | ||
| "OPTION_TENSOR_PARALLEL_DEGREE": "8", | ||
| }, | ||
| "ModelDataSource": { | ||
| "S3DataSource": { | ||
| "S3Uri": ANY, | ||
| "S3DataType": "S3Prefix", | ||
| "CompressionType": "None", | ||
| "ModelAccessConfig": {"AcceptEula": True}, | ||
| } | ||
| }, | ||
| }, | ||
| vpc_config=None, | ||
| enable_network_isolation=False, # should be set to false | ||
| tags=ANY, | ||
| ) | ||
| mock_endpoint_from_production_variants.assert_called_once_with( | ||
| name=ANY, | ||
| production_variants=ANY, | ||
| tags=ANY, | ||
| kms_key=ANY, | ||
| vpc_config=ANY, | ||
| enable_network_isolation=False, | ||
| role=ANY, | ||
| live_logging=False, # this should be set to false for IC | ||
| wait=True, | ||
| ) | ||
| mock_create_inference_component.assert_called_once() | ||
|
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||
|
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| def test_js_model_with_optimize_quantization_on_pre_optimized_model_requests_are_expected( | ||
| sagemaker_session, | ||
| ): | ||
| with patch.object( | ||
| Session, | ||
| "wait_for_optimization_job", | ||
| return_value={"OptimizationJobName": "mock_optimization_job"}, | ||
| ), patch.object( | ||
| Session, "create_model", return_value="mock_model" | ||
| ) as mock_create_model, patch.object( | ||
| Session, "endpoint_from_production_variants", return_value="mock_endpoint_name" | ||
| ) as mock_endpoint_from_production_variants: | ||
| iam_client = sagemaker_session.boto_session.client("iam") | ||
| role_arn = iam_client.get_role(RoleName=ROLE_NAME)["Role"]["Arn"] | ||
|
|
||
| sagemaker_session.sagemaker_client.create_optimization_job = MagicMock() | ||
|
|
||
| schema_builder = SchemaBuilder("test", "test") | ||
| model_builder = ModelBuilder( | ||
| model="meta-textgeneration-llama-3-1-8b-instruct", | ||
| schema_builder=schema_builder, | ||
| sagemaker_session=sagemaker_session, | ||
| role_arn=role_arn, | ||
| ) | ||
|
|
||
| optimized_model = model_builder.optimize( | ||
| instance_type="ml.g5.xlarge", # set to small instance in case a network call is made | ||
| quantization_config={ | ||
| "OverrideEnvironment": { | ||
| "OPTION_QUANTIZE": "fp8", | ||
| }, | ||
| }, | ||
| accept_eula=True, | ||
| ) | ||
|
|
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| optimized_model.deploy() | ||
|
|
||
| mock_create_model.assert_called_once_with( | ||
| name=ANY, | ||
| role=ANY, | ||
| container_defs={ | ||
| "Image": ANY, | ||
| "Environment": { | ||
| "SAGEMAKER_PROGRAM": "inference.py", | ||
| "ENDPOINT_SERVER_TIMEOUT": "3600", | ||
| "MODEL_CACHE_ROOT": "/opt/ml/model", | ||
| "SAGEMAKER_ENV": "1", | ||
| "HF_MODEL_ID": "/opt/ml/model", | ||
| "SAGEMAKER_MODEL_SERVER_WORKERS": "1", | ||
| "OPTION_QUANTIZE": "fp8", | ||
| }, | ||
| "ModelDataSource": { | ||
| "S3DataSource": { | ||
| "S3Uri": ANY, | ||
| "S3DataType": "S3Prefix", | ||
| "CompressionType": "None", | ||
| "ModelAccessConfig": {"AcceptEula": True}, | ||
| } | ||
| }, | ||
| }, | ||
| vpc_config=None, | ||
| enable_network_isolation=True, # should be set to false | ||
| tags=ANY, | ||
| ) | ||
| mock_endpoint_from_production_variants.assert_called_once() |
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