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# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from typing import TYPE_CHECKING
import torch
from loguru import logger
import ttnn
from ..parallel.config import MochiVAEParallelConfig, vae_neighbor_pad
from ..parallel.manager import CCLManager
from .module import Module, Parameter
if TYPE_CHECKING:
from collections.abc import Sequence
def get_conv3d_config(in_channels, grid_size, weights_dtype=ttnn.bfloat16):
shape_to_blocking = {
# (60, 106, 768): (128, 96, 1, 2, 16),
# (120, 212, 512): (128, 128, 1, 8, 4),
# (240, 424, 256): (128, 128, 4, 4, 2),
# (480, 848, 128): (128, 128, 1, 2, 16),
768: (128, 96, 1, 2, 16),
512: (128, 128, 1, 8, 4),
256: (128, 128, 4, 4, 2),
128: (128, 128, 1, 2, 16),
}
if weights_dtype == ttnn.float32:
C_in_block, C_out_block, T_out_block, H_out_block, W_out_block = 32, 32, 1, 1, 1
else:
blocking = shape_to_blocking.get((in_channels), None)
if blocking is None:
C_in_block, C_out_block, T_out_block, H_out_block, W_out_block = 128, 32, 1, 2, 16
logger.warning(
f"No blocking found for input shape {in_channels}. Using default blocking: {C_in_block}, {C_out_block}, {T_out_block}, {H_out_block}, {W_out_block}"
)
else:
C_in_block, C_out_block, T_out_block, H_out_block, W_out_block = blocking
return ttnn.Conv3dConfig(
weights_dtype=weights_dtype,
output_layout=ttnn.ROW_MAJOR_LAYOUT,
T_out_block=T_out_block,
W_out_block=W_out_block,
H_out_block=H_out_block,
C_out_block=C_out_block,
C_in_block=C_in_block,
compute_with_storage_grid_size=grid_size,
)
class ContextParallelConv3d(Module):
def __init__(
self,
in_channels: int,
out_channels: int,
*,
kernel_size: Sequence[int],
stride: Sequence[int] = (1, 1, 1),
bias: bool = True,
causal: bool = True,
context_parallel: bool = True,
groups: int = 1,
padding_mode: str,
mesh_device: ttnn.MeshDevice,
parallel_config: MochiVAEParallelConfig,
ccl_manager: CCLManager,
dtype: ttnn.DataType = ttnn.bfloat16,
) -> None:
super().__init__()
self.dtype = dtype
self.halos_chunk_map = {
768: 30, # 16
512: 30, # 64
256: 60, # 96
128: 120, # 192,
}
assert causal
assert context_parallel
assert padding_mode in ["zeros", "replicate"]
assert groups == 1
self.mesh_device = mesh_device
self.grid_size = mesh_device.compute_with_storage_grid_size()
self.in_channels = in_channels
self.out_channels = out_channels
self.causal = causal
self.context_parallel = context_parallel
self.parallel_config = parallel_config
self.ccl_manager = ccl_manager
# Conv3d parameters
self.kernel_size = kernel_size
self.stride = stride
self.padding_mode = padding_mode
self.groups = groups
# Calculate padding
if self.parallel_config.h_parallel.factor > 1:
height_pad = 0
else:
height_pad = (self.kernel_size[1] - 1) // 2
if self.parallel_config.w_parallel.factor > 1:
width_pad = 0
else:
width_pad = (self.kernel_size[2] - 1) // 2
self.padding = (0, height_pad, width_pad)
d = self.kernel_size[0] * self.kernel_size[1] * self.kernel_size[2] * self.in_channels
self.weight = Parameter(total_shape=[d, self.out_channels], device=mesh_device, pad_value=0, dtype=dtype)
self.bias = (
Parameter(total_shape=[1, self.out_channels], device=mesh_device, pad_value=0, dtype=dtype)
if bias
else None
)
self.compute_kernel_config = ttnn.WormholeComputeKernelConfig(
math_fidelity=ttnn.MathFidelity.HiFi2,
math_approx_mode=False,
fp32_dest_acc_en=True,
packer_l1_acc=False,
)
self.conv_config = get_conv3d_config(
self.in_channels,
self.grid_size,
weights_dtype=dtype,
)
def _prepare_torch_state(self, state: dict[str, torch.Tensor]) -> None:
if "weight" in state:
weight_tt = ttnn.from_torch(state["weight"], dtype=self.dtype, pad_value=0)
prepared = ttnn.experimental.prepare_conv3d_weights(
weight_tensor=weight_tt, C_in_block=self.conv_config.C_in_block, device=self.mesh_device
)
state["weight"] = ttnn.to_torch(ttnn.get_device_tensors(prepared)[0])
if "bias" in state:
state["bias"] = state["bias"].reshape(1, -1)
def _causal_pad_input(self, x_NTHWC, pad_front, pad_back=0):
"""
Input is either a mult-device tensor (when num_devices == 1) or a single-device tensor (when num_devices > 1).
"""
T = x_NTHWC.shape[1]
if self.padding_mode == "zeros":
# Do front and back padding
x_pad_NTHWC = ttnn.pad(x_NTHWC, (0, 0, 0, 0, pad_front, pad_back), value=0.0)
ttnn.deallocate(x_NTHWC)
elif self.padding_mode == "replicate":
# Use concat to pad
front_slice = x_NTHWC[:, 0:1, :, :, :]
x_pad_NTHWC = ttnn.concat(
[front_slice] * pad_front + [x_NTHWC],
dim=1,
)
ttnn.deallocate(x_NTHWC)
T_pad = x_pad_NTHWC.shape[1]
assert T_pad == T + pad_front + pad_back
return x_pad_NTHWC
def forward(self, x_NTHWC: ttnn.Tensor) -> ttnn.Tensor:
# Compute temporal padding amounts
context_size = self.kernel_size[0] - 1
if self.causal:
pad_front = context_size
pad_back = 0
else:
assert False, "Non-causal padding is not supported"
# pad_front = context_size // 2
# pad_back = context_size - pad_front
if self.parallel_config.time_parallel.factor == 1:
# Single-device padding
x_pad_NTHWC = self._causal_pad_input(x_NTHWC, pad_front, pad_back)
else:
# Multi-device padding. Input is fractured on dim=1 (T) and must pass frames between devices
# Pad on first device
halo_tensor = ttnn.squeeze(x_NTHWC, 0)
halo_tensor = vae_neighbor_pad(
self.ccl_manager,
halo_tensor,
cluster_axis=self.parallel_config.time_parallel.mesh_axis,
dim=0,
padding_left=2,
padding_right=0,
padding_mode="replicate",
)
x_pad_NTHWC = ttnn.unsqueeze(halo_tensor, 0)
out_NTHWC = ttnn.experimental.conv3d(
input_tensor=x_pad_NTHWC,
weight_tensor=self.weight.data,
bias_tensor=self.bias.data if self.bias is not None else None,
device=self.mesh_device,
config=self.conv_config,
output_channels=self.out_channels,
kernel_size=self.kernel_size,
stride=self.stride,
padding=self.padding,
padding_mode=self.padding_mode,
dtype=self.dtype,
groups=self.groups,
compute_kernel_config=self.compute_kernel_config,
)
return out_NTHWC
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