多卡的逻辑

Great question. Let’s clarify the logic behind multi-GPU (multi-card) training (single server) and multi-server distributed training, as well as how data, gradients, and model aggregation are handled.

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1. Multi-GPU (Single Node) Training

When you use multiple GPUs on a single server (e.g., 4 GPUs in one machine), the standard approach is data parallelism (PyTorch nn.DataParallel or DistributedDataParallel). Here’s the pipeline:

Step-by-step Logic:

1. Data Split:

   • In each batch, your data loader splits the batch into 4 mini-batches (one per GPU).

2. Forward Pass (Parallel):

   • Each GPU receives its mini-batch and computes the forward pass independently.

3. Backward Pass (Gradient Calculation):

   • Each GPU computes the gradients for its mini-batch locally.

4. Gradient Synchronization (All-Reduce):

   • After computing local gradients, all GPUs synchronize their gradients (average or sum them across all GPUs).
   • This is often called all-reduce. After this, each GPU has the same, averaged gradients.

5. Optimizer Step (Model Update):

   • Each GPU updates its local model parameters (which are now the same across GPUs).

Summary:
You do not aggregate models per se—you aggregate (average) gradients after backward, then update the models in sync.

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2. Multi-Node (Multi-Server) Distributed Training

When you scale across multiple servers (each with 1 or more GPUs), the logic is similar but requires networking:

Step-by-step Logic:

1. Data Split:

   • The dataset is partitioned so that each server/GPU gets different data for each batch (no overlap).

2. Forward/Backward Pass:

   • Each GPU (on each server) computes the forward and backward pass on its own mini-batch.

3. Gradient Synchronization (All-Reduce Across Servers):

   • Gradients are synchronized across all GPUs on all servers (typically using NCCL, Gloo, or MPI).
   • This is network-intensive, so network speed matters.

4. Optimizer Step:

   • Model parameters are updated after gradient averaging.

Summary:
The logic is the same—aggregate gradients, then update. Each model replica (across all GPUs, all servers) stays in sync.

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3. Other Strategies (Model Parallelism, Parameter Server)

• Model Parallelism:
  Model is split across GPUs (not common for standard vision/NLP tasks, more for very large models).
• Parameter Server:
  A central server collects and averages gradients (more common in older or asynchronous frameworks).

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4. Visual Diagram

+----------+      +----------+      +----------+      +----------+
|  GPU 0   |      |  GPU 1   |      |  GPU 2   |      |  GPU 3   |
|  Data 0  |      |  Data 1  |      |  Data 2  |      |  Data 3  |
+----------+      +----------+      +----------+      +----------+
     \                |                |                /
      \               |                |               /
           (All-Reduce Gradient Sync)
                    |
             (Each GPU now has same gradients)
                    |
              (Optimizer Step: parameters updated)

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5. Summary Table

Setting	Data Distribution	Gradient Sync	Model Update
Single Node	Split across GPUs	All-reduce across GPUs	Locally on each GPU
Multi-Node	Split across servers	All-reduce (network)	Locally on each GPU
Model Parallel	Split across layers/parts	N/A or custom	N/A or custom

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Key Takeaway

• Data is split. Each GPU computes forward/backward. Gradients are averaged (synchronized). Models are updated in sync.
• You do NOT aggregate the models at the end of each step—you synchronize the gradients and keep the models in sync every step.

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If you want code examples or have a specific framework in mind (PyTorch, TensorFlow, etc.), let me know!