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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from megatron.core import ModelParallelConfig, parallel_state
from torch.distributed import _functional_collectives as funcol
from torch.distributed._functional_collectives import all_reduce
from cosmos_predict1.autoregressive.modules.linear import ColumnParallelLinear, RowParallelLinear
def compute_llama3_ffn_hidden_dim(dim: int, multiple_of: int, ffn_dim_multiplier: float) -> int:
"""
Computes the feedforward network dimensionality.
Args:
dim (int): The embedding dimensionality.
multiple_of (int): The multiple to round up the hidden dimensionality.
ffn_dim_multiplier (float): The multiplier for the hidden dimensionality.
Returns:
The feedforward network dimensionality.
"""
hidden_dim = 4 * dim
hidden_dim = int(2 * hidden_dim / 3) # custom dim factor
hidden_dim = int(ffn_dim_multiplier * hidden_dim)
# Round up hidden dimensionality to the nearest multiple
return multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
class MLP(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
tensor_parallel_size: int = 1,
):
"""
Initializes the multilayer perceptron (MLP) module.
Args:
dim: The input and output dimensionality.
hidden_dim: The dimensionality of the hidden layer.
"""
super().__init__()
self.tp_size = tensor_parallel_size
self.w1 = nn.Linear(dim, hidden_dim // self.tp_size, bias=False)
self.w2 = nn.Linear(hidden_dim // self.tp_size, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim // self.tp_size, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Performs the forward pass of the MLP module.
Args:
x: The input tensor of shape (batch_size, dim).
Returns:
The output tensor of shape (batch_size, dim).
"""
output = self.w2(F.silu(self.w1(x)) * self.w3(x))
if self.tp_size > 1:
output = all_reduce(output, "sum", group=parallel_state.get_tensor_model_parallel_group())
return output
class TrainingMLP(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
hidden_dropout: float = 0.0,
set_parallel_mode: bool = False,
model_parallel: Optional[ModelParallelConfig] = None,
inference: bool = False,
):
"""
Initializes the multilayer perceptron (MLP) module.
Args:
dim: The input and output dimensionality.
hidden_dim: The dimensionality of the hidden layer.
hidden_dropout: Dropout after the attention and feed-forward layers (following TransformerEngine's
implementation in its TransformerLayer class).
set_parallel_mode: Whether to use column and row parallel linear layers.
model_parallel: The model parallel configuration.
inference: Whether the model is used for inference.
"""
super().__init__()
self.hidden_dropout = hidden_dropout
if model_parallel and model_parallel.tensor_model_parallel_size > 1:
self.tp_size = model_parallel.tensor_model_parallel_size
else:
self.tp_size = 1
if set_parallel_mode and not inference:
kwargs = {"bias": False, "init_method": lambda x: x, "config": model_parallel}
# Using column and row parallel linear layers
self.w1 = ColumnParallelLinear(dim, hidden_dim, gather_output=False, **kwargs)
self.w2 = RowParallelLinear(hidden_dim, dim, input_is_parallel=True, skip_bias_add=True, **kwargs)
self.w3 = ColumnParallelLinear(dim, hidden_dim, gather_output=False, **kwargs)
else:
self.w1 = nn.Linear(dim, hidden_dim // self.tp_size, bias=False)
self.w2 = nn.Linear(hidden_dim // self.tp_size, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim // self.tp_size, bias=False)
self.inference = inference
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Performs the forward pass of the MLP module.
Args:
x: The input tensor of shape (batch_size, dim).
Returns:
The output tensor of shape (batch_size, dim).
"""
x = F.dropout(x, p=self.hidden_dropout, training=self.training)
output = self.w2(F.silu(self.w1(x)) * self.w3(x))
output = F.dropout(output, p=self.hidden_dropout, training=self.training)
if self.inference and self.tp_size > 1:
output = funcol.all_reduce(output, "sum", group=parallel_state.get_tensor_model_parallel_group())
return output
def init_weights(self, init_std: float):
"""
Initializes the weights of the MLP module.
"""
nn.init.trunc_normal_(self.w1.weight, mean=0.0, std=0.02)
for linear in (self.w2, self.w3):
nn.init.trunc_normal_(linear.weight, mean=0.0, std=init_std)
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