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"""
Example: Using BitLinear as a drop-in replacement for nn.Linear in a Transformer.
This example demonstrates:
1. Creating a simple Transformer block with standard nn.Linear
2. Converting it to use BitLinear layers
3. Running forward passes to verify compatibility
4. Comparing memory usage and output similarity
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
from bitlinear import BitLinear, MultiTernaryLinear, convert_linear_to_bitlinear
class TransformerBlock(nn.Module):
"""
Simplified Transformer block for demonstration.
Contains:
- Multi-head self-attention with linear projections
- Feed-forward network with two linear layers
- Layer normalization and residual connections
"""
def __init__(
self,
d_model: int = 512,
nhead: int = 8,
dim_feedforward: int = 2048,
dropout: float = 0.1,
):
super().__init__()
# Multi-head attention components
self.d_model = d_model
self.nhead = nhead
self.d_k = d_model // nhead
# Linear projections for Q, K, V
self.q_proj = nn.Linear(d_model, d_model)
self.k_proj = nn.Linear(d_model, d_model)
self.v_proj = nn.Linear(d_model, d_model)
self.out_proj = nn.Linear(d_model, d_model)
# Feed-forward network
self.ffn = nn.Sequential(
nn.Linear(d_model, dim_feedforward),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(dim_feedforward, d_model),
)
# Layer normalization
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
# Dropout
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
def forward(
self,
x: torch.Tensor,
mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
Forward pass through Transformer block.
Args:
x: Input tensor [batch_size, seq_len, d_model]
mask: Optional attention mask
Returns:
Output tensor [batch_size, seq_len, d_model]
"""
# Multi-head self-attention
residual = x
x = self.norm1(x)
# Compute Q, K, V
q = self.q_proj(x)
k = self.k_proj(x)
v = self.v_proj(x)
# Reshape for multi-head attention
batch_size, seq_len, _ = x.shape
q = q.view(batch_size, seq_len, self.nhead, self.d_k).transpose(1, 2)
k = k.view(batch_size, seq_len, self.nhead, self.d_k).transpose(1, 2)
v = v.view(batch_size, seq_len, self.nhead, self.d_k).transpose(1, 2)
# Scaled dot-product attention
scores = torch.matmul(q, k.transpose(-2, -1)) / (self.d_k ** 0.5)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
attn_weights = F.softmax(scores, dim=-1)
attn_output = torch.matmul(attn_weights, v)
# Reshape and project back
attn_output = attn_output.transpose(1, 2).contiguous().view(
batch_size, seq_len, self.d_model
)
attn_output = self.out_proj(attn_output)
attn_output = self.dropout1(attn_output)
# First residual connection
x = residual + attn_output
# Feed-forward network
residual = x
x = self.norm2(x)
x = self.ffn(x)
x = self.dropout2(x)
# Second residual connection
x = residual + x
return x
def count_parameters(model: nn.Module) -> int:
"""Count total trainable parameters in a model."""
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def estimate_memory_mb(model: nn.Module) -> float:
"""Estimate memory usage of model parameters in MB."""
total_bytes = sum(p.numel() * p.element_size() for p in model.parameters())
return total_bytes / (1024 ** 2)
def compare_outputs(
output1: torch.Tensor,
output2: torch.Tensor,
) -> dict:
"""
Compare two output tensors and compute similarity metrics.
Returns:
Dictionary with comparison metrics
"""
mse = F.mse_loss(output1, output2).item()
cosine_sim = F.cosine_similarity(
output1.flatten(), output2.flatten(), dim=0
).item()
relative_error = (
torch.norm(output1 - output2) / torch.norm(output1)
).item()
return {
"mse": mse,
"cosine_similarity": cosine_sim,
"relative_error": relative_error,
}
def main():
"""Main example demonstrating BitLinear usage in Transformer."""
print("=" * 80)
print("BitLinear Transformer Example")
print("=" * 80)
# Configuration
batch_size = 32
seq_len = 128
d_model = 512
nhead = 8
dim_feedforward = 2048
# Create input
x = torch.randn(batch_size, seq_len, d_model)
print(f"\nInput shape: {x.shape}")
# 1. Create standard Transformer block
print("\n" + "-" * 80)
print("1. Standard Transformer with nn.Linear")
print("-" * 80)
model_standard = TransformerBlock(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
)
print(f"Parameters: {count_parameters(model_standard):,}")
print(f"Memory: {estimate_memory_mb(model_standard):.2f} MB")
# Forward pass
with torch.no_grad():
output_standard = model_standard(x)
print(f"Output shape: {output_standard.shape}")
# 2. Convert to BitLinear
print("\n" + "-" * 80)
print("2. Transformer with BitLinear")
print("-" * 80)
model_bitlinear = convert_linear_to_bitlinear(model_standard, inplace=False)
print(f"Parameters: {count_parameters(model_bitlinear):,}")
print(f"Memory: {estimate_memory_mb(model_bitlinear):.2f} MB")
# Forward pass
with torch.no_grad():
output_bitlinear = model_bitlinear(x)
print(f"Output shape: {output_bitlinear.shape}")
# 3. Compare outputs
print("\n" + "-" * 80)
print("3. Output Comparison")
print("-" * 80)
metrics = compare_outputs(output_standard, output_bitlinear)
print(f"MSE: {metrics['mse']:.6f}")
print(f"Cosine similarity: {metrics['cosine_similarity']:.6f}")
print(f"Relative error: {metrics['relative_error']:.6f}")
# 4. Memory savings
print("\n" + "-" * 80)
print("4. Memory Savings")
print("-" * 80)
mem_standard = estimate_memory_mb(model_standard)
mem_bitlinear = estimate_memory_mb(model_bitlinear)
savings = (mem_standard - mem_bitlinear) / mem_standard * 100
print(f"Standard model: {mem_standard:.2f} MB")
print(f"BitLinear model: {mem_bitlinear:.2f} MB")
print(f"Memory savings: {savings:.1f}%")
print(f"Compression ratio: {mem_standard / mem_bitlinear:.1f}x")
# 5. Count Linear layers converted
print("\n" + "-" * 80)
print("5. Conversion Details")
print("-" * 80)
def count_linear_layers(model):
count = 0
for module in model.modules():
if isinstance(module, nn.Linear):
count += 1
return count
def count_bitlinear_layers(model):
count = 0
for module in model.modules():
if isinstance(module, BitLinear):
count += 1
return count
print(f"Original Linear layers: {count_linear_layers(model_standard)}")
print(f"Converted BitLinear layers: {count_bitlinear_layers(model_bitlinear)}")
print("\n" + "=" * 80)
print("Example complete!")
print("=" * 80)
print("\nKey Takeaways:")
print("- BitLinear is a drop-in replacement for nn.Linear")
print("- Significant memory savings (~20x for weights)")
print("- Output similarity is high (cosine sim > 0.99 typically)")
print("- Slight accuracy trade-off due to ternary quantization")
if __name__ == "__main__":
main()
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