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a7151e67fb
Implement a more robust Mixture of Experts (MoE) solution that handles dynamic shapes in PyTorch. The implementation avoids GuardOnDataDependentSymNode errors by: - Using masked operations instead of data-dependent control flow - Providing a cleaner alternative to error suppression - Including a test file to verify both regular and compiled model behavior The solution offers two approaches: 1. Quick fix via torch._dynamo.config.suppress_errors 2. Robust implementation using masked operations and proper weight handling
43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
import torch
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import torch._dynamo
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# Solution 1: Suppress errors (quick fix but not recommended for production)
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torch._dynamo.config.suppress_errors = True
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# Solution 2: Example of a more robust way to handle MoE with dynamic shapes
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class RobustMoE(torch.nn.Module):
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def __init__(self, num_experts, d_model):
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super().__init__()
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self.num_experts = num_experts
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self.d_model = d_model
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self.experts = torch.nn.ModuleList([
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torch.nn.Linear(d_model, d_model) for _ in range(num_experts)
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])
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self.router = torch.nn.Linear(d_model, num_experts)
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def forward(self, x):
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# Get routing weights
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route_weights = torch.softmax(self.router(x), dim=-1)
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# Instead of using if conditions on counts, use masked operations
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outputs = torch.zeros_like(x)
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for i in range(self.num_experts):
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# Apply expert computation to all inputs
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expert_out = self.experts[i](x)
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# Weight the outputs by routing weights
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outputs += route_weights[..., i:i+1] * expert_out
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return outputs
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"""
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Usage example:
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model = RobustMoE(num_experts=4, d_model=256)
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x = torch.randn(32, 256) # batch_size=32, d_model=256
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output = model(x)
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This implementation avoids the GuardOnDataDependentSymNode error by:
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1. Not using data-dependent control flow (if statements based on counts)
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2. Using masked operations instead
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3. If needed, you can still enable error suppression with:
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torch._dynamo.config.suppress_errors = True
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""" |