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on
Zero
Running
on
Zero
"""k-diffusion transformer diffusion models, version 2. | |
Codes adopted from https://github.com/crowsonkb/k-diffusion | |
""" | |
from contextlib import contextmanager | |
import math | |
import threading | |
state = threading.local() | |
state.flop_counter = None | |
def flop_counter(enable=True): | |
try: | |
old_flop_counter = state.flop_counter | |
state.flop_counter = FlopCounter() if enable else None | |
yield state.flop_counter | |
finally: | |
state.flop_counter = old_flop_counter | |
class FlopCounter: | |
def __init__(self): | |
self.ops = [] | |
def op(self, op, *args, **kwargs): | |
self.ops.append((op, args, kwargs)) | |
def flops(self): | |
flops = 0 | |
for op, args, kwargs in self.ops: | |
flops += op(*args, **kwargs) | |
return flops | |
def op(op, *args, **kwargs): | |
if getattr(state, "flop_counter", None): | |
state.flop_counter.op(op, *args, **kwargs) | |
def op_linear(x, weight): | |
return math.prod(x) * weight[0] | |
def op_attention(q, k, v): | |
*b, s_q, d_q = q | |
*b, s_k, d_k = k | |
*b, s_v, d_v = v | |
return math.prod(b) * s_q * s_k * (d_q + d_v) | |
def op_natten(q, k, v, kernel_size): | |
*q_rest, d_q = q | |
*_, d_v = v | |
return math.prod(q_rest) * (d_q + d_v) * kernel_size**2 |