Blame for code/qwen_lm.py

# Copyright (c) 2024 Alibaba Inc
#
# 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.nn as nn
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import Qwen2ForCausalLM, Qwen2Config
from torch.nn.utils.rnn import pad_sequence, unpad_sequence
from tqdm import tqdm

def topk_sampling(weighted_scores, decoded_tokens, top_k=1024):
    zeros = weighted_scores.new_ones(weighted_scores.shape) * float('-inf')
    values,indices =  torch.topk(weighted_scores,top_k)
    zeros.scatter_(-1, indices, values)
    return random_sampling(zeros,decoded_tokens)

# Repetition Aware Sampling in VALL-E 2

def ras_sampling(weighted_scores, decoded_tokens, top_p=0.8, top_k=50, win_size=10, tau_r=0.1):
    top_ids = nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k)
    rep_num = (torch.tensor(decoded_tokens[-win_size:]).to(weighted_scores.device) == top_ids).sum().item()
    if rep_num >= win_size * tau_r:
        top_ids = random_sampling(weighted_scores, decoded_tokens)
    return top_ids

def caras_sampling(weighted_scores, decoded_tokens, top_p=0.8, top_k=25, win_size=10, tau_r=0.1):
    weighted_scores, cfg_weighted_scores = weighted_scores
    top_ids = nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k)
    rep_num = (torch.tensor(decoded_tokens[-win_size:]).to(weighted_scores.device) == top_ids).sum().item()
    if rep_num >= win_size * tau_r:
        top_ids = random_sampling(cfg_weighted_scores, decoded_tokens)
    return top_ids

def nucleus_sampling(weighted_scores, top_p=0.8, top_k=25):
    prob, indices = [], []
    cum_prob = 0.0
    sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True)
    for i in range(len(sorted_idx)):
        # sampling both top-p and numbers.
        if cum_prob < top_p and len(prob) < top_k:
            cum_prob += sorted_value[i]
            prob.append(sorted_value[i])
            indices.append(sorted_idx[i])
        else:
            break
    prob = torch.tensor(prob).to(weighted_scores)
    indices = torch.tensor(indices, dtype=torch.long).to(weighted_scores.device)
    top_ids = indices[prob.multinomial(1, replacement=True)]
    return top_ids


def random_sampling(weighted_scores, decoded_tokens):
    top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True)
    return top_ids

def th_accuracy(pad_outputs: torch.Tensor, pad_targets: torch.Tensor,
                ignore_label: int) -> torch.Tensor:
    """Calculate accuracy.

    Args:
        pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
        pad_targets (LongTensor): Target label tensors (B, Lmax).
        ignore_label (int): Ignore label id.

    Returns:
        torch.Tensor: Accuracy value (0.0 - 1.0).

    """
    pad_pred = pad_outputs.view(pad_targets.size(0), pad_targets.size(1),
                                pad_outputs.size(1)).argmax(2)
    mask = pad_targets != ignore_label
    numerator = torch.sum(
        pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
    denominator = torch.sum(mask)
    return (numerator / denominator).detach()


def subsequent_mask(
        size: int,
        device: torch.device = torch.device("cpu"),
) -> torch.Tensor:
    """Create mask for subsequent steps (size, size).

    This mask is used only in decoder which works in an auto-regressive mode.
    This means the current step could only do attention with its left steps.

    In encoder, fully attention is used when streaming is not necessary and
    the sequence is not long. In this  case, no attention mask is needed.

    When streaming is need, chunk-based attention is used in encoder. See
    subsequent_chunk_mask for the chunk-based attention mask.

    Args:
        size (int): size of mask
        str device (str): "cpu" or "cuda" or torch.Tensor.device
        dtype (torch.device): result dtype

    Returns:
        torch.Tensor: mask

    Examples:
        >>> subsequent_mask(3)
        [[1, 0, 0],
         [1, 1, 0],
         [1, 1, 1]]
    """
    arange = torch.arange(size, device=device)
    mask = arange.expand(size, size)
    arange = arange.unsqueeze(-1)
    mask = mask <= arange
    return mask


def subsequent_chunk_mask(
        size: int,
        chunk_size: int,
        num_left_chunks: int = -1,
        device: torch.device = torch.device("cpu"),
) -> torch.Tensor:
    """Create mask for subsequent steps (size, size) with chunk size,
       this is for streaming encoder

    Args:
        size (int): size of mask
        chunk_size (int): size of chunk
        num_left_chunks (int): number of left chunks
            <0: use full chunk
            >=0: use num_left_chunks
        device (torch.device): "cpu" or "cuda" or torch.Tensor.device

    Returns:
        torch.Tensor: mask

    Examples:
        >>> subsequent_chunk_mask(4, 2)
        [[1, 1, 0, 0],
         [1, 1, 0, 0],
         [1, 1, 1, 1],
         [1, 1, 1, 1]]
    """
    ret = torch.zeros(size, size, device=device, dtype=torch.bool)
    for i in range(size):
        if num_left_chunks < 0:
            start = 0
        else:
            start = max((i // chunk_size - num_left_chunks) * chunk_size, 0)
        ending = min((i // chunk_size + 1) * chunk_size, size)
        ret[i, start:ending] = True
    return ret


def add_optional_chunk_mask(xs: torch.Tensor,
                            masks: torch.Tensor,
                            use_dynamic_chunk: bool,
                            use_dynamic_left_chunk: bool,
                            decoding_chunk_size: int,
                            static_chunk_size: int,
                            num_decoding_left_chunks: int,
                            enable_full_context: bool = True):
    """ Apply optional mask for encoder.

    Args:
        xs (torch.Tensor): padded input, (B, L, D), L for max length
        mask (torch.Tensor): mask for xs, (B, 1, L)
        use_dynamic_chunk (bool): whether to use dynamic chunk or not
        use_dynamic_left_chunk (bool): whether to use dynamic left chunk for
            training.
        decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's
            0: default for training, use random dynamic chunk.
            <0: for decoding, use full chunk.
            >0: for decoding, use fixed chunk size as set.
        static_chunk_size (int): chunk size for static chunk training/decoding
            if it's greater than 0, if use_dynamic_chunk is true,
            this parameter will be ignored
        num_decoding_left_chunks: number of left chunks, this is for decoding,
            the chunk size is decoding_chunk_size.
            >=0: use num_decoding_left_chunks
            <0: use all left chunks
        enable_full_context (bool):
            True: chunk size is either [1, 25] or full context(max_len)
            False: chunk size ~ U[1, 25]

    Returns:
        torch.Tensor: chunk mask of the input xs.
    """
    # Whether to use chunk mask or not
    if use_dynamic_chunk:
        max_len = xs.size(1)
        if decoding_chunk_size < 0:
            chunk_size = max_len
            num_left_chunks = -1
        elif decoding_chunk_size > 0:
            chunk_size = decoding_chunk_size
            num_left_chunks = num_decoding_left_chunks
        else:
            # chunk size is either [1, 25] or full context(max_len).
            # Since we use 4 times subsampling and allow up to 1s(100 frames)
            # delay, the maximum frame is 100 / 4 = 25.
            chunk_size = torch.randint(1, max_len, (1, )).item()
            num_left_chunks = -1
            if chunk_size > max_len // 2 and enable_full_context:
                chunk_size = max_len
            else:
                chunk_size = chunk_size % 25 + 1
                if use_dynamic_left_chunk:
                    max_left_chunks = (max_len - 1) // chunk_size
                    num_left_chunks = torch.randint(0, max_left_chunks,
                                                    (1, )).item()
        chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size,
                                            num_left_chunks,
                                            xs.device)  # (L, L)
        chunk_masks = chunk_masks.unsqueeze(0)  # (1, L, L)
        chunk_masks = masks & chunk_masks  # (B, L, L)
    elif static_chunk_size > 0:
        num_left_chunks = num_decoding_left_chunks
        chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size,
                                            num_left_chunks,
                                            xs.device)  # (L, L)
        chunk_masks = chunk_masks.unsqueeze(0)  # (1, L, L)
        chunk_masks = masks & chunk_masks  # (B, L, L)
    else:
        chunk_masks = masks
    return chunk_masks


def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
    """Make mask tensor containing indices of padded part.

    See description of make_non_pad_mask.

    Args:
        lengths (torch.Tensor): Batch of lengths (B,).
    Returns:
        torch.Tensor: Mask tensor containing indices of padded part.

    Examples:
        >>> lengths = [5, 3, 2]
        >>> make_pad_mask(lengths)
        masks = [[0, 0, 0, 0 ,0],
                 [0, 0, 0, 1, 1],
                 [0, 0, 1, 1, 1]]
    """
    batch_size = lengths.size(0)
    max_len = max_len if max_len > 0 else lengths.max().item()
    seq_range = torch.arange(0,
                             max_len,
                             dtype=torch.int64,
                             device=lengths.device)
    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
    seq_length_expand = lengths.unsqueeze(-1)
    mask = seq_range_expand >= seq_length_expand
    return mask

qwen_conf = {
    "architectures": [
    "Qwen2ForCausalLM"
    ],
    "attention_dropout": 0.0,
    "bos_token_id": 151643,
    "eos_token_id": 151643,
    "hidden_act": "silu",
    "hidden_size": 896,
    "initializer_range": 0.02,
    "intermediate_size": 4864,
    "max_position_embeddings": 32768,
    "max_window_layers": 24,
    "model_type": "qwen2",
    "num_attention_heads": 14,
    "num_hidden_layers": 24,
    "num_key_value_heads": 2,
    "rms_norm_eps": 1e-06,
    "rope_theta": 1000000.0,
    "sliding_window": 32768,
    "tie_word_embeddings": True,
    "torch_dtype": "bfloat16",
    "transformers_version": "4.40.1",
    "use_cache": True,
    "use_mrope": False,
    "use_sliding_window": False,
    "vocab_size": 1024
}

# qwen_conf = {
#     "architectures": [
#     "Qwen2ForCausalLM"
#     ],
#     "attention_dropout": 0.0,
#     "bos_token_id": 151643,
#     "eos_token_id": 151643,
#     "hidden_act": "silu",
#     "hidden_size": 768,
#     "initializer_range": 0.02,
#     "intermediate_size": 3072,
#     "max_position_embeddings": 32768,
#     "max_window_layers": 24,
#     "model_type": "qwen2",
#     "num_attention_heads": 12,
#     "num_hidden_layers": 24,
#     "num_key_value_heads": 2,
#     "rms_norm_eps": 1e-06,
#     "rope_theta": 1000000.0,
#     "sliding_window": 32768,
#     "tie_word_embeddings": True,
#     "torch_dtype": "bfloat16",
#     "transformers_version": "4.40.1",
#     "use_cache": True,
#     "use_mrope": False,
#     "use_sliding_window": False,
#     "vocab_size": 1024
# }

DTYPES = {
    "bf16": torch.bfloat16,
    "fp16": torch.float16,
}

IGNORE_ID = -100
MAX_TEXT_TOKEN = 1024
BERT_DIM = 1024
SPK_EMB_DIM = 256

class QwenEmbeddingEncoder(nn.Module):
    def __init__(
            self,
            dtype: str = "bf16"
    ):
        super(QwenEmbeddingEncoder, self).__init__()
        self.dtype = DTYPES.get(dtype, torch.float32)
        config = Qwen2Config(**qwen_conf)
        self.model = AutoModelForCausalLM.from_config(config)
        self._output_size = self.model.config.hidden_size

    def output_size(self) -> int:
        return self._output_size

    def forward(
            self,
            input_embeds: torch.Tensor,
            ilens: torch.Tensor,
    ):
        input_masks = (~make_pad_mask(ilens)).to(input_embeds.device).long()

        outs = self.model(
            inputs_embeds=input_embeds,
            attention_mask=input_masks,
            output_hidden_states=True,
            return_dict=True,
        )

        return outs.hidden_states[-1], input_masks

    def forward_one_step(self, xs, masks, cache=None):
        outs = self.model(
            inputs_embeds=xs,
            attention_mask=masks,
            output_hidden_states=True,
            return_dict=True,
            use_cache=True,
            past_key_values=cache,
        )
        xs = outs.hidden_states[-1]
        new_cache = outs.past_key_values
        assert new_cache is not None
        return xs, masks, new_cache


class LLM(torch.nn.Module):
    def __init__(
            self,
            audio_token_size: int,
            dtype: str = "fp32",
    ):
        super().__init__()
        self.dtype = DTYPES.get(dtype, torch.float32)

        # 1. build text token inputs related modules

        self.llm = QwenEmbeddingEncoder(dtype=dtype)
        self.llm.model.model.embed_tokens = None
        self.llm_input_size = self.llm.model.config.hidden_size
        self.llm_output_size = self.llm.model.config.hidden_size
        self.audio_token_size = audio_token_size

        self.llm_decoder = nn.Linear(self.llm_output_size, audio_token_size + 1)
        self.criterion_ce = torch.nn.CrossEntropyLoss(ignore_index=IGNORE_ID)
        # 3. [Optional] build audio token related modules
        self.speech_embedding = torch.nn.Embedding(audio_token_size, self.llm_input_size)
        self.text_embedding = nn.Embedding(MAX_TEXT_TOKEN, self.llm_input_size)
        self.bert_embedding = nn.Linear(BERT_DIM, self.llm_input_size)
        self.spk_embedding =  nn.Linear(SPK_EMB_DIM, self.llm_input_size)

        self.audio_bos = nn.Parameter(torch.randn(1, self.llm_input_size))

    def forward(
            self,
            text_tokens,
            bert_features,
            spk_features,
            prosody_tokens,
            lengths,
    ):
        """
        Args:
            text_tokens: (B, L)
            bert_features: (B, T, 1024)
            spk_features: (B, 256)
            prosody_tokens: (B, L)
            lengths: (B,)
        """
        device = text_tokens.device
        bsz = text_tokens.size(0)

        # get input embeddings
        spk_embedding = self.spk_embedding(spk_features)  # [b, 256] -> [b, h]

        text_embedding = self.text_embedding(text_tokens)  # [b, t] -> [b, t, h]
        bert_embedding = self.bert_embedding(bert_features)  # [b, t, 1024] -> [b, t, h]
        text_embedding = text_embedding + bert_embedding

        audio_embedding = self.speech_embedding(prosody_tokens)  #[b, t] -> [b, t, h]

        # get lm input and target
        lm_input = []
        lm_target = []
        for b in range(bsz):
            # cond part
            spk_emb = spk_embedding[b].unsqueeze(0)  # [h] -> [1, h]
            text_emb = text_embedding[b][:lengths[b]]  # [t, h]

            # ar input
            audio_emb = audio_embedding[b][
                        :lengths[b]]  # [t, h]
            audio_emb = torch.cat([self.audio_bos, audio_emb], dim=0) # [t+1, h]

            cur_inp = torch.cat([spk_emb, text_emb, audio_emb], dim=0)  # [2t+2, h]
            lm_input.append(cur_inp)

            # ar target
            cur_target = prosody_tokens[b][:lengths[b]] # [t]
            eos = torch.tensor([self.audio_token_size], dtype=torch.int64).to(device)
            cur_target = torch.cat([cur_target, eos], dim=0) # [t+1]

            cur_target = torch.nn.functional.pad(cur_target, (cur_inp.shape[0] - cur_target.size(0), 0),
                                                 value=IGNORE_ID) # [2t+2]
            lm_target.append(cur_target)


        lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32).to(device)
        lm_input = pad_sequence(lm_input, batch_first=True, padding_value=IGNORE_ID)
        lm_target = pad_sequence(lm_target, batch_first=True, padding_value=IGNORE_ID)

        # 6. run lm forward
        lm_output, lm_output_mask = self.llm(lm_input.to(self.dtype), lm_input_len.to(device))
        logits = self.llm_decoder(lm_output)
        loss = self.criterion_ce(logits.view(-1, self.audio_token_size + 1), lm_target.view(-1))
        # print(loss,lm_input.shape)

        acc = th_accuracy(logits.view(-1, self.audio_token_size + 1), lm_target, ignore_label=IGNORE_ID)

        return {'loss': loss, 'acc': acc}

    @torch.inference_mode()
    @torch.no_grad()
    def inference(
            self,
            text_tokens,
            bert_features,
            spk_features,
            prompt_audio_token=None,
            sampling_method=None,
            sampling_kwargs=None,
    ):
        device = text_tokens.device
        if prompt_audio_token is not None:
            res_tokens = prompt_audio_token.clone()
            audio_embedding = self.speech_embedding(prompt_audio_token) # [b, t] -> [b, t, h]
            audio_embedding = torch.cat([self.audio_bos.unsqueeze(0), audio_embedding], dim=1) # [b, t+1, h]

        else:
            res_tokens = torch.zeros((text_tokens.size(0), 0), dtype=torch.int64).to(device)
            audio_embedding = self.audio_bos.unsqueeze(0) # [1, 1, h]


        # get input embeddings
        spk_embedding = self.spk_embedding(spk_features)  # [b, 256] -> [b, h]

        text_embedding = self.text_embedding(text_tokens)  # [b, t] -> [b, t, h]
        bert_embedding = self.bert_embedding(bert_features)  # [b, t, 1024] -> [b, t, h]
        text_embedding = text_embedding + bert_embedding


        # cond part
        spk_embedding = spk_embedding.unsqueeze(0)  # [b, h] -> [b, 1, h]


        lm_input = torch.cat([spk_embedding, text_embedding, audio_embedding], dim=1)
        # if cfg_scale != 1.0:
        #     print("infer with cfg_scale:", cfg_scale)
        #     lm_cf_input = torch.cat([0*clap_embedding, 0*text_embedding, audio_embedding], dim=1)
        #     lm_input = torch.cat([lm_input, lm_cf_input], dim=0)
        # 4. cal min/max_length

        max_len = text_tokens.shape[-1]

        # 5. step by step decode
        out_tokens = []
        offset = 0
        state = None

        for i in tqdm(range(int(max_len+1))):
            y_pred, _, state = self.llm.forward_one_step(lm_input.to(self.dtype),
                                                         torch.ones(lm_input.shape[0], lm_input.shape[1],
                                                                    device=lm_input.device).to(torch.bool), cache=state)
            logits = self.llm_decoder(y_pred[:, -1])
            # if cfg_scale != 1.0:
            #     # perform context free guidance
            #     logits_cf = logits[1]
            #     logits = logits[0]
            #     infer_cfg_ratio = cfg_scale
            #     logits = infer_cfg_ratio * logits + (1 - infer_cfg_ratio) * logits_cf

            logp = logits.log_softmax(dim=-1)
            logp = logp.squeeze(dim=0)

            # if i < int(min_len):
            #     logp[self.audio_token_size] = torch.tensor(float('-inf'), dtype=self.dtype)

            top_ids = sampling_method(logp, out_tokens, **sampling_kwargs).item()

            if top_ids == self.audio_token_size:
                print('end!')
                break

            # # in stream mode, yield token one by one

            current_token = torch.tensor([[top_ids]], dtype=torch.int64, device=device)
            res_tokens = torch.cat([res_tokens, current_token], dim=1)
            out_tokens.append(top_ids)
            offset += lm_input.size(1)
            lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)
            # if cfg_scale != 1.0:
            #     lm_input = lm_input.repeat(2, 1, 1)
        
        if res_tokens.shape[-1]!=max_len:
            print('infer error', res_tokens.shape, max_len)
            res_tokens = res_tokens[:,:max_len]
            res_tokens = torch.nn.functional.pad(res_tokens, (0, max_len-res_tokens.size(1)), value=0)
        return res_tokens

if __name__ == '__main__':

    audio_token_size=1024
    device = 'cuda'
    llm = LLM(audio_token_size=audio_token_size).to(device)
    print('n_params:', sum(p.numel() for p in llm.parameters() if p.requires_grad)/1e6, 'M')
    text = torch.randint(0, 1024, (2, 11)).to(device)
    bert = torch.randn(2, 11, 1024).to(device)
    spk = torch.randn(2, 256).to(device)
    prosody = torch.randint(0, 1024, (2, 11)).to(device)
    lengths = torch.tensor([4, 11]).to(device)

    llm.train()
    res = llm(text, bert, spk, prosody, lengths)
    loss = res['loss']
    acc = res['acc']
    print('loss:', loss, 'acc:', acc)
    loss.backward()
    for name, param in llm.named_parameters():
        if param.grad is  None:
            print('name:', name, 'has no grad')

    llm.eval()
    text = text[:1]
    bert = bert[:1]
    spk = spk[:1]
    prosody = prosody[:1]

    res = llm.inference(text, bert, spk, None, topk_sampling, {'top_k': 1024})
    print('sampling res', res.shape)