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sd_pndm_scheduler.py
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sd_pndm_scheduler.py
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# SPDX-FileCopyrightText: © 2023 Tenstorrent Inc.
# SPDX-License-Identifier: Apache-2.0
import math
from typing import List, Optional, Tuple, Union
from dataclasses import dataclass
import numpy as np
import torch
import ttnn
@dataclass
class TtSchedulerOutput:
prev_sample: ttnn.Tensor
class TtPNDMScheduler:
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
skip_prk_steps: bool = False,
set_alpha_to_one: bool = False,
prediction_type: str = "epsilon",
steps_offset: int = 0,
device=None,
):
self.num_train_timesteps = num_train_timesteps
self.steps_offset = steps_offset
self.skip_prk_steps = skip_prk_steps
self.prediction_type = prediction_type
self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
self.device = device
self.alphas = 1.0 - self.betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
self.alphas_cumprod = self.alphas_cumprod.tolist()
self.init_noise_sigma = 1.0
self.pndm_order = 4
# running values
self.cur_model_output = 0
self.counter = 0
self.cur_sample = None
self.ets = []
# setable values
self.num_inference_steps = None
self._timesteps = np.arange(0, num_train_timesteps)[::-1].copy()
self.prk_timesteps = None
self.plms_timesteps = None
self.timesteps = None
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
self.num_inference_steps = num_inference_steps
step_ratio = self.num_train_timesteps // self.num_inference_steps
self._timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()
self._timesteps += self.steps_offset
if self.skip_prk_steps:
self.prk_timesteps = np.array([])
self.plms_timesteps = np.concatenate([self._timesteps[:-1], self._timesteps[-2:-1], self._timesteps[-1:]])[
::-1
].copy()
else:
prk_timesteps = np.array(self._timesteps[-self.pndm_order :]).repeat(2) + np.tile(
np.array([0, self.num_train_timesteps // num_inference_steps // 2]), self.pndm_order
)
self.prk_timesteps = (prk_timesteps[:-1].repeat(2)[1:-1])[::-1].copy()
self.plms_timesteps = self._timesteps[:-3][::-1].copy()
timesteps = np.concatenate([self.prk_timesteps, self.plms_timesteps]).astype(np.int64)
self.timesteps = torch.from_numpy(timesteps).to(device)
self.prk_timesteps = self.prk_timesteps.tolist()
self.ets = []
self.counter = 0
def step(
self,
model_output: ttnn.Tensor,
timestep: int,
sample: ttnn.Tensor,
return_dict: bool = True,
) -> Union[TtSchedulerOutput, Tuple]:
if self.counter < len(self.prk_timesteps) and not self.skip_prk_steps:
return self.step_prk(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict)
else:
return self.step_plms(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict)
def step_prk(
self,
model_output: ttnn.Tensor,
timestep: int,
sample: ttnn.Tensor,
return_dict: bool = True,
) -> Union[TtSchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
diff_to_prev = 0 if self.counter % 2 else self.num_train_timesteps // self.num_inference_steps // 2
prev_timestep = timestep - diff_to_prev
timestep = self.prk_timesteps[self.counter // 4 * 4]
if self.counter % 4 == 0:
self.cur_model_output += ttnn.multiply(model_output, 1 / 6)
self.ets.append(model_output)
self.cur_sample = sample
elif (self.counter - 1) % 4 == 0:
self.cur_model_output += ttnn.multiply(model_output, 1 / 3)
elif (self.counter - 2) % 4 == 0:
self.cur_model_output += ttnn.multiply(model_output, 1 / 3)
elif (self.counter - 3) % 4 == 0:
model_output = ttnn.add(self.cur_model_output, ttnn.multiply(model_output, 1 / 6))
self.cur_model_output = 0
# cur_sample should not be `None`
cur_sample = self.cur_sample if self.cur_sample is not None else sample
prev_sample = self._get_prev_sample(cur_sample, timestep, prev_timestep, model_output)
self.counter += 1
if not return_dict:
return (prev_sample,)
return TtSchedulerOutput(prev_sample=prev_sample)
def step_plms(
self,
model_output: ttnn.Tensor,
timestep: int,
sample: ttnn.Tensor,
return_dict: bool = True,
) -> Union[TtSchedulerOutput, Tuple]:
prev_timestep = timestep - self.num_train_timesteps // self.num_inference_steps
if self.counter != 1:
self.ets = self.ets[-3:]
self.ets.append(model_output)
else:
prev_timestep = timestep
timestep = timestep + self.num_train_timesteps // self.num_inference_steps
if len(self.ets) == 1 and self.counter == 0:
model_output = model_output
self.cur_sample = sample
elif len(self.ets) == 1 and self.counter == 1:
model_output = ttnn.mul((model_output + self.ets[-1]), 1 / 2)
sample = self.cur_sample
self.cur_sample = None
elif len(self.ets) == 2:
model_output = ttnn.mul((3 * self.ets[-1] - self.ets[-2]), 1 / 2)
elif len(self.ets) == 3:
model_output = ttnn.mul((23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]), 1 / 12)
else:
model_output = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
prev_sample = self._get_prev_sample(sample, timestep, prev_timestep, model_output)
self.counter += 1
if not return_dict:
return (prev_sample,)
return TtSchedulerOutput(prev_sample=prev_sample)
def _get_prev_sample(self, sample, timestep, prev_timestep, model_output):
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
if self.prediction_type == "v_prediction":
model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
elif self.prediction_type != "epsilon":
raise ValueError(
f"prediction_type given as {self.prediction_type} must be one of `epsilon` or `v_prediction`"
)
sample_coeff = (alpha_prod_t_prev / alpha_prod_t) ** (0.5)
model_output_denom_coeff = alpha_prod_t * beta_prod_t_prev ** (0.5) + (
alpha_prod_t * beta_prod_t * alpha_prod_t_prev
) ** (0.5)
prev_sample = sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * model_output * (
1 / model_output_denom_coeff
)
return prev_sample