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https://github.com/deepseek-ai/DeepSeek-V3.git
synced 2025-04-20 10:38:57 -04:00
bc9459df40
BREAKING CHANGE: Restructured model.py into dedicated modules under inference/models/ Key Changes: - Split monolithic model.py into focused, single-responsibility modules: - config.py: Model configuration and hyperparameters - attention.py: Multi-head Latent Attention (MLA) implementation - moe.py: Mixture of Experts components (Gate, Expert, MoE) - linear.py: Linear layer variants with parallel processing support - __init__.py: Clean public API exports Benefits: - Improved code organization and maintainability - Better separation of concerns - Enhanced testability of individual components - Clearer dependency management - Simplified future modifications and extensions Migration: - Update imports to use new module structure - No functional changes to existing implementations - Backwards compatible with current model weights
28 lines
1.0 KiB
Python
28 lines
1.0 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.distributed as dist
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from ..kernel import act_quant, weight_dequant, fp8_gemm
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class Linear(nn.Module):
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dtype = torch.bfloat16
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def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
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# ... (Linear implementation)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# ... (Linear forward implementation)
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class ColumnParallelLinear(Linear):
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def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
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# ... (ColumnParallelLinear implementation)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# ... (ColumnParallelLinear forward implementation)
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class RowParallelLinear(Linear):
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def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
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# ... (RowParallelLinear implementation)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# ... (RowParallelLinear forward implementation) |