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2bbe3e1a69
Replace 9 old tutorials with 12 new numbered tutorials (00-11) covering roofline through full-stack audit. Redesign landing page, add models-and-solvers and extending-the-engine guides. Add __main__.py, cli.py, and cli/ package for command-line interface.
69 lines
4.1 KiB
Plaintext
69 lines
4.1 KiB
Plaintext
# core.solver.DistributedModel { #mlsysim.core.solver.DistributedModel }
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```python
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core.solver.DistributedModel()
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```
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Resolves fleet-wide communication, synchronization, and pipelining constraints.
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This solver models the constraints of distributed scale for distributed training. It
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decomposes a workload across a cluster using 3D Parallelism (DP, TP, PP)
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and calculates the resulting communication overheads and idle times
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(bubbles) that determine the Model FLOPs Utilization (MFU).
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Literature Source:
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1. Shoeybi et al. (2019), "Megatron-LM: Training Multi-Billion Parameter
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Language Models Using Model Parallelism." (3D Parallelism Framework)
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2. Narayanan et al. (2019), "PipePipe: Efficient Pipeline Parallelism for
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Training Large Models." (1F1B Pipeline Bubble Model)
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3. Patarasuk & Mueller (2009), "Bandwidth-Optimal All-Reduce Algorithms
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for Clusters of Workstations." (Ring All-Reduce)
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## Methods
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| Name | Description |
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| --- | --- |
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| [solve](#mlsysim.core.solver.DistributedModel.solve) | Calculates distributed training performance using the 3D/4D Parallelism model. |
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### solve { #mlsysim.core.solver.DistributedModel.solve }
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```python
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core.solver.DistributedModel.solve(
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model,
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fleet,
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batch_size=1,
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precision='fp16',
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efficiency=0.5,
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tp_size=1,
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pp_size=1,
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ep_size=1,
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v_stages=1,
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microbatch_count=1,
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topology_override=None,
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)
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```
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Calculates distributed training performance using the 3D/4D Parallelism model.
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#### Parameters {.doc-section .doc-section-parameters}
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| Name | Type | Description | Default |
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|-------------------|----------|------------------------------------------------------------------------------------------------------------------------------|------------|
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| model | Workload | The model architecture to simulate. | _required_ |
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| fleet | Fleet | The hardware cluster and network topology. | _required_ |
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| batch_size | int | Global batch size. | `1` |
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| precision | str | Numerical precision (fp16, fp32, int8). | `'fp16'` |
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| efficiency | float | Achieved compute efficiency (0.0 to 1.0). | `0.5` |
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| tp_size | int | Tensor Parallelism degree. Splits individual layers across GPUs, usually within a single node over high-speed NVLink. | `1` |
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| pp_size | int | Pipeline Parallelism degree. Chains model layers across multiple nodes, introducing 'pipeline bubbles' while saving memory. | `1` |
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| ep_size | int | Expert Parallelism degree for MoE models. Introduces All-to-All communication overhead across nodes. | `1` |
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| v_stages | int | Number of virtual stages for interleaved pipeline schedules. | `1` |
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| microbatch_count | int | Number of microbatches (M). Increasing M reduces the pipeline bubble but increases synchronization overhead. | `1` |
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| topology_override | str | Force a specific topology (ring, tree). | `None` |
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#### Returns {.doc-section .doc-section-returns}
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| Name | Type | Description |
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|--------|------------------|-----------------------------------------------------------------------------------------------------|
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| | Dict\[str, Any\] | Metrics including DP/TP/EP latency, the Pipeline Bubble penalty, and the final Scaling Efficiency. |
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