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import torch
import torch.nn as nn
class MockTransformerBlock(nn.Module):
def __init__(self, d_model):
super().__init__()
self.d_model = d_model
# Standard projection layers
self.q_proj = nn.Linear(d_model, d_model, bias=False)
self.v_proj = nn.Linear(d_model, d_model, bias=False)
# Layernorm parameter (1D multiplier scale)
self.norm = nn.Parameter(torch.ones(d_model))
def forward(self, x):
# Normalization
x_norm = x * self.norm
# Projection
q = self.q_proj(x_norm)
v = self.v_proj(x_norm)
return q + v
def run_proof():
print("======================================================================")
print("ZYMATICA | Zero-RAM Meta: JIT Swapping & Memory Optimization Proof")
print("======================================================================\n")
d_model = 128
print("[1] Instantiating Model Block on META Device (0 RAM/VRAM)...")
with torch.device("meta"):
block = MockTransformerBlock(d_model)
print(f" - Block class: {block.__class__.__name__}")
print(f" - Parameter Devices:")
for name, param in block.named_parameters():
print(f" * {name:15s} | Shape: {list(param.shape)} | Device: {param.device} (Allocated: {param.nbytes} bytes on meta)")
# 2. Strict Shape-Filtered Initializer
print("\n[2] Applying Strict Shape-Filtered Initializers...")
for name, param in list(block.named_parameters()):
# Identify layernorm multipliers vs heavy matrices
if len(param.shape) == 1:
# Concrete memory load (restore to CPU) by replacing parameter
new_param = nn.Parameter(torch.ones(param.shape, device="cpu"))
if "." in name:
submod_name, param_attr = name.rsplit(".", 1)
submod = block.get_submodule(submod_name)
setattr(submod, param_attr, new_param)
else:
setattr(block, name, new_param)
print(f" * [FILTERED LOAD] restored '{name}' to CPU parameter.")
else:
print(f" * [DEFERRED] '{name}' remains on device: {param.device}")
# 3. JIT Swapping Forward Pass Execution
print("\n[3] Simulating Autoregressive JIT Swap Execution...")
x_input = torch.randn(1, d_model, device="cpu")
print(f" - Input tensor shape: {x_input.shape} | Device: {x_input.device}")
# Hook Simulation: JIT Swap target weight projections into CPU/CUDA RAM
print(" -> Intercepting Block forward: Loading factors and inflating weights...")
temp_q_weight = torch.randn(d_model, d_model)
temp_v_weight = torch.randn(d_model, d_model)
# Store reference to meta parameters
meta_q_param = block.q_proj.weight
meta_v_param = block.v_proj.weight
# Assign concrete weights for the forward pass duration
block.q_proj.weight = nn.Parameter(temp_q_weight)
block.q_proj.weight.layer_idx = 0
block.v_proj.weight = nn.Parameter(temp_v_weight)
block.v_proj.weight.layer_idx = 0
print(f" - Parameter Devices during computation:")
print(f" * q_proj.weight | Device: {block.q_proj.weight.device} (Active: {block.q_proj.weight.nbytes:,} bytes)")
print(f" * v_proj.weight | Device: {block.v_proj.weight.device} (Active: {block.v_proj.weight.nbytes:,} bytes)")
# Run forward pass
y_output = block(x_input)
print(f" - Forward computation completed. Output norm: {y_output.norm().item():.4f}")
# Post-hook: Swap parameter buffers back to meta context
print(" -> Freeing Layer buffers: Returning parameters to Meta Context...")
block.q_proj.weight = meta_q_param
block.v_proj.weight = meta_v_param
print(f" - Parameter Devices after cleanup:")
print(f" * q_proj.weight | Device: {block.q_proj.weight.device}")
print(f" * v_proj.weight | Device: {block.v_proj.weight.device}")
print("\n[VERIFICATION] Zero-RAM JIT swapping pipeline verified.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Zymatica Zero-RAM Meta Proof")
parser.add_argument("--test", action="store_true", help="Run test mode")
args = parser.parse_args()
run_proof()
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