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Feat: Optimize() validations across TRT, VLLM, Neuron container optimizations #4927

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Issue #, if available:

Description of changes:

Testing done:

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@@ -1157,6 +1161,15 @@ def optimize(
Model: A deployable ``Model`` object.
"""

# TODO: ideally these dictionaries need to be sagemaker_core shapes
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TODO: Ideally validations should happen at the start of the flow, not after everything is set up in the wrapper. Lets fast follow this and gradually shift the validations to helper functions in the outer scope here

logger = logging.getLogger(__name__)


class OptimizationContainer(Enum):
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@Lokiiiiii Lokiiiiii Nov 14, 2024

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We can use these for internal organization. But we should not burden customers with knowing how our optimizations work, which containers are used under which scenarios, etc.

Consider the following error messages -

  • Optimizations that use Compilation and Speculative Decoding are not currently supported on GPU & Neuron instances.
  • Optimizations that use Compilation and Quantization are not currently supported on Neuron instances.
  • Optimizations that use Quantization:SmoothQuant are only supported with Compilation on GPU instances.
  • Sharding is mutually exclusive and only supported on GPU instances.

The general theme we want to maintain is

Optimizations {A,B,C} are {not|only} supported with optimizations {X,Y,Z} on instances {L,M,N}

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5 participants