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Zero
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# Copyright (c) 2025 Tsinghua Univ. (authors: Xingchen Song)
#
# 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.
import torch
from torch import nn
from transformers import AutoConfig
from flashcosyvoice.config import CosyVoice2LLMConfig
from flashcosyvoice.modules.qwen2_components.layers import (
ParallelLMHead, Qwen2DecoderLayer, RMSNorm, VocabParallelEmbedding)
class Qwen2Model(nn.Module):
def __init__(
self,
config: CosyVoice2LLMConfig,
):
super().__init__()
self.vocab_size = config.vocab_size
self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([Qwen2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
) -> torch.Tensor:
hidden_states = self.embed_tokens(input_ids)
residual = None
for layer in self.layers:
hidden_states, residual = layer(
positions,
hidden_states,
residual,
)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
class Qwen2ForCausalLM(nn.Module):
packed_modules_mapping = {
"q_proj": ("qkv_proj", "q"),
"k_proj": ("qkv_proj", "k"),
"v_proj": ("qkv_proj", "v"),
"gate_proj": ("gate_up_proj", 0),
"up_proj": ("gate_up_proj", 1),
}
def __init__(
self,
config: CosyVoice2LLMConfig | AutoConfig
):
super().__init__()
self.model = Qwen2Model(config)
if hasattr(config, "speech_vocab_size"):
self.lm_head = ParallelLMHead(config.speech_vocab_size, config.hidden_size, bias=getattr(config, "lm_head_bias", True))
self.model_type = "speech_llm"
else:
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size, bias=False)
self.model_type = "text_llm"
self.tie_word_embeddings = config.tie_word_embeddings
if self.tie_word_embeddings:
if self.model_type == "speech_llm":
assert config.vocab_size == config.speech_vocab_size, "vocab_size and speech_vocab_size must be the same when tie_word_embeddings is True"
self.lm_head.weight.data = self.model.embed_tokens.weight.data
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions)
return hidden_states
def compute_logits(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
logits = self.lm_head(hidden_states)
return logits
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