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| import re | |
| import sys | |
| import math | |
| import uuid | |
| import torch | |
| import types | |
| import contextlib | |
| import numpy as np | |
| import torch.nn.functional as F | |
| from torch import nn | |
| from omegaconf import DictConfig, open_dict | |
| class Dictionary: | |
| def __init__(self, *args, **kwargs): | |
| pass | |
| fairseq = types.ModuleType("fairseq") | |
| fairseq_data = types.ModuleType("fairseq.data") | |
| fairseq_data_dictionary = types.ModuleType("fairseq.data.dictionary") | |
| fairseq_data_dictionary.Dictionary = Dictionary | |
| fairseq.data = fairseq_data | |
| fairseq_data.dictionary = fairseq_data_dictionary | |
| sys.modules["fairseq"] = fairseq | |
| sys.modules["fairseq.data"] = fairseq_data | |
| sys.modules["fairseq.data.dictionary"] = fairseq_data_dictionary | |
| def load_model(filename): | |
| state = torch.load(filename, map_location="cpu") | |
| model = HubertModel(HubertConfig(**state['cfg']['model'])) | |
| model.load_state_dict(state['model'], strict=False) | |
| return [model], Model_Config(state["cfg"]), Model_Config(state["cfg"]["task"]) | |
| def softmax(x, dim, onnx_trace = False): | |
| return F.softmax(x.float(), dim=dim) if onnx_trace else F.softmax(x, dim=dim, dtype=torch.float32) | |
| def log_softmax(x, dim, onnx_trace = False): | |
| return F.log_softmax(x.float(), dim=dim) if onnx_trace else F.log_softmax(x, dim=dim, dtype=torch.float32) | |
| def eval_str_dict(x, type=dict): | |
| if x is None: return None | |
| if isinstance(x, str): x = eval(x) | |
| return x | |
| def with_incremental_state(cls): | |
| cls.__bases__ = (FairseqIncrementalState,) + tuple(b for b in cls.__bases__ if b != FairseqIncrementalState) | |
| return cls | |
| def quant_noise(module, p, block_size): | |
| if p <= 0: return module | |
| assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d)) | |
| is_conv = module.weight.ndim == 4 | |
| if not is_conv: assert (module.weight.size(1) % block_size == 0) | |
| else: | |
| if module.kernel_size == (1, 1): assert (module.in_channels % block_size == 0) | |
| else: | |
| k = module.kernel_size[0] * module.kernel_size[1] | |
| assert k % block_size == 0 | |
| def _forward_pre_hook(mod, input): | |
| if mod.training: | |
| if not is_conv: | |
| weight = mod.weight | |
| in_features = weight.size(1) | |
| out_features = weight.size(0) | |
| mask = torch.zeros(in_features // block_size * out_features, device=weight.device) | |
| mask.bernoulli_(p) | |
| mask = mask.repeat_interleave(block_size, -1).view(-1, in_features) | |
| else: | |
| weight = mod.weight | |
| in_channels = mod.in_channels | |
| out_channels = mod.out_channels | |
| if mod.kernel_size == (1, 1): | |
| mask = torch.zeros(int(in_channels // block_size * out_channels), device=weight.device) | |
| mask.bernoulli_(p) | |
| mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels) | |
| else: | |
| mask = torch.zeros(weight.size(0), weight.size(1), device=weight.device) | |
| mask.bernoulli_(p) | |
| mask = (mask.unsqueeze(2).unsqueeze(3).repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1])) | |
| mask = mask.to(torch.bool) | |
| s = 1 / (1 - p) | |
| mod.weight.data = s * weight.masked_fill(mask, 0) | |
| module.register_forward_pre_hook(_forward_pre_hook) | |
| return module | |
| class FairseqDropout(nn.Module): | |
| def __init__(self, p, module_name=None): | |
| super().__init__() | |
| self.p = p | |
| self.module_name = module_name | |
| self.apply_during_inference = False | |
| def forward(self, x, inplace = False): | |
| return F.dropout(x, p=self.p, training=True, inplace=inplace) if self.p > 0 and (self.training or self.apply_during_inference) else x | |
| def make_generation_fast_(self, name, retain_dropout = False, retain_dropout_modules = None, **kwargs): | |
| if retain_dropout: | |
| if (retain_dropout_modules is None or self.module_name in retain_dropout_modules): self.apply_during_inference = True | |
| class FairseqIncrementalState(object): | |
| def __init__(self, *args, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| self.init_incremental_state() | |
| def init_incremental_state(self): | |
| self._incremental_state_id = str(uuid.uuid4()) | |
| def _get_full_incremental_state_key(self, key): | |
| return "{}.{}".format(self._incremental_state_id, key) | |
| def get_incremental_state(self, incremental_state, key): | |
| full_key = self._get_full_incremental_state_key(key) | |
| if incremental_state is None or full_key not in incremental_state: return None | |
| return incremental_state[full_key] | |
| def set_incremental_state(self, incremental_state, key, value): | |
| if incremental_state is not None: incremental_state[self._get_full_incremental_state_key(key)] = value | |
| return incremental_state | |
| class FairseqDecoder(nn.Module): | |
| def __init__(self, dictionary): | |
| super().__init__() | |
| self.dictionary = dictionary | |
| self.onnx_trace = False | |
| self.adaptive_softmax = None | |
| def forward(self, prev_output_tokens, encoder_out=None, **kwargs): | |
| x, extra = self.extract_features(prev_output_tokens, encoder_out=encoder_out, **kwargs) | |
| return self.output_layer(x), extra | |
| def extract_features(self, prev_output_tokens, encoder_out=None, **kwargs): | |
| pass | |
| def output_layer(self, features, **kwargs): | |
| pass | |
| def get_normalized_probs(self, net_output, log_probs, sample = None): | |
| return self.get_normalized_probs_scriptable(net_output, log_probs, sample) | |
| def get_normalized_probs_scriptable(self, net_output, log_probs, sample = None): | |
| if hasattr(self, "adaptive_softmax") and self.adaptive_softmax is not None: | |
| if sample is not None: | |
| assert "target" in sample | |
| target = sample["target"] | |
| else: target = None | |
| out = self.adaptive_softmax.get_log_prob(net_output[0], target=target) | |
| return out.exp_() if not log_probs else out | |
| logits = net_output[0] | |
| return log_softmax(logits, dim=-1, onnx_trace=self.onnx_trace) if log_probs else softmax(logits, dim=-1, onnx_trace=self.onnx_trace) | |
| def max_positions(self): | |
| return 1e6 | |
| def upgrade_state_dict_named(self, state_dict, name): | |
| return state_dict | |
| def prepare_for_onnx_export_(self): | |
| self.onnx_trace = True | |
| class FairseqIncrementalDecoder(FairseqDecoder): | |
| def __init__(self, dictionary): | |
| super().__init__(dictionary) | |
| def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs): | |
| pass | |
| def extract_features(self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs): | |
| pass | |
| def reorder_incremental_state(self, incremental_state, new_order): | |
| pass | |
| def reorder_incremental_state_scripting(self, incremental_state, new_order): | |
| for module in self.modules(): | |
| if hasattr(module, "reorder_incremental_state"): | |
| result = module.reorder_incremental_state(incremental_state, new_order) | |
| if result is not None: incremental_state = result | |
| def set_beam_size(self, beam_size): | |
| if getattr(self, "_beam_size", -1) != beam_size: | |
| seen = set() | |
| def apply_set_beam_size(module): | |
| if (module != self and hasattr(module, "set_beam_size") and module not in seen): | |
| seen.add(module) | |
| module.set_beam_size(beam_size) | |
| self.apply(apply_set_beam_size) | |
| self._beam_size = beam_size | |
| class MultiheadAttention(FairseqIncrementalDecoder): | |
| def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0.0, bias=True, add_bias_kv=False, add_zero_attn=False, self_attention=False, encoder_decoder_attention=False, dictionary=None, q_noise=0.0, qn_block_size=8, xformers_att_config=None, xformers_blocksparse_layout=None, xformers_blocksparse_blocksize=16): | |
| super().__init__(dictionary) | |
| xformers_att_config = eval_str_dict(xformers_att_config) | |
| self.use_xformers = xformers_att_config is not None | |
| if self.use_xformers: raise ImportError | |
| self.embed_dim = embed_dim | |
| self.kdim = kdim if kdim is not None else embed_dim | |
| self.vdim = vdim if vdim is not None else embed_dim | |
| self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim | |
| self.num_heads = num_heads | |
| self.dropout_module = FairseqDropout(dropout, module_name=self.__class__.__name__) | |
| self.head_dim = embed_dim // num_heads | |
| assert (self.head_dim * num_heads == self.embed_dim) | |
| self.scaling = self.head_dim**-0.5 | |
| self.self_attention = self_attention | |
| self.encoder_decoder_attention = encoder_decoder_attention | |
| assert not self.self_attention or self.qkv_same_dim | |
| self.k_proj = quant_noise(nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size) | |
| self.v_proj = quant_noise(nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size) | |
| self.q_proj = quant_noise(nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size) | |
| self.out_proj = quant_noise(nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size) | |
| if add_bias_kv: | |
| self.bias_k = nn.Parameter(torch.Tensor(1, 1, embed_dim)) | |
| self.bias_v = nn.Parameter(torch.Tensor(1, 1, embed_dim)) | |
| else: self.bias_k = self.bias_v = None | |
| self.add_zero_attn = add_zero_attn | |
| self.beam_size = 1 | |
| self.reset_parameters() | |
| self.onnx_trace = False | |
| self.skip_embed_dim_check = False | |
| self.init_incremental_state() | |
| def prepare_for_onnx_export_(self): | |
| self.onnx_trace = True | |
| def reset_parameters(self): | |
| if self.qkv_same_dim: | |
| nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2)) | |
| nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2)) | |
| nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2)) | |
| else: | |
| nn.init.xavier_uniform_(self.k_proj.weight) | |
| nn.init.xavier_uniform_(self.v_proj.weight) | |
| nn.init.xavier_uniform_(self.q_proj.weight) | |
| nn.init.xavier_uniform_(self.out_proj.weight) | |
| if self.out_proj.bias is not None: nn.init.constant_(self.out_proj.bias, 0.0) | |
| if self.bias_k is not None: nn.init.xavier_normal_(self.bias_k) | |
| if self.bias_v is not None: nn.init.xavier_normal_(self.bias_v) | |
| def _get_reserve_head_index(self, num_heads_to_keep: int): | |
| k_proj_heads_norm, q_proj_heads_norm, v_proj_heads_norm = [], [], [] | |
| for i in range(self.num_heads): | |
| start_idx = i * self.head_dim | |
| end_idx = (i + 1) * self.head_dim | |
| k_proj_heads_norm.append(torch.sum(torch.abs(self.k_proj.weight[start_idx:end_idx])).tolist() + torch.sum(torch.abs(self.k_proj.bias[start_idx:end_idx])).tolist()) | |
| q_proj_heads_norm.append(torch.sum(torch.abs(self.q_proj.weight[start_idx:end_idx])).tolist() + torch.sum(torch.abs(self.q_proj.bias[start_idx:end_idx])).tolist()) | |
| v_proj_heads_norm.append(torch.sum(torch.abs(self.v_proj.weight[start_idx:end_idx])).tolist() + torch.sum(torch.abs(self.v_proj.bias[start_idx:end_idx])).tolist()) | |
| heads_norm = [] | |
| for i in range(self.num_heads): | |
| heads_norm.append(k_proj_heads_norm[i] + q_proj_heads_norm[i] + v_proj_heads_norm[i]) | |
| sorted_head_index = sorted(range(self.num_heads), key=lambda k: heads_norm[k], reverse=True) | |
| reserve_head_index = [] | |
| for i in range(num_heads_to_keep): | |
| reserve_head_index.append((sorted_head_index[i] * self.head_dim, (sorted_head_index[i] + 1) * self.head_dim)) | |
| return reserve_head_index | |
| def _adaptive_prune_heads(self, reserve_head_index): | |
| new_q_weight, new_q_bias, new_k_weight, new_k_bias, new_v_weight, new_v_bias, new_out_proj_weight = [], [], [], [], [], [], [] | |
| for ele in reserve_head_index: | |
| start_idx, end_idx = ele | |
| new_q_weight.append(self.q_proj.weight[start_idx:end_idx]) | |
| new_q_bias.append(self.q_proj.bias[start_idx:end_idx]) | |
| new_k_weight.append(self.k_proj.weight[start_idx:end_idx]) | |
| new_k_bias.append(self.k_proj.bias[start_idx:end_idx]) | |
| new_v_weight.append(self.v_proj.weight[start_idx:end_idx]) | |
| new_v_bias.append(self.v_proj.bias[start_idx:end_idx]) | |
| new_out_proj_weight.append(self.out_proj.weight[:, start_idx:end_idx]) | |
| new_q_weight = torch.cat(new_q_weight).detach() | |
| new_k_weight = torch.cat(new_k_weight).detach() | |
| new_v_weight = torch.cat(new_v_weight).detach() | |
| new_out_proj_weight = torch.cat(new_out_proj_weight, dim=-1).detach() | |
| new_q_weight.requires_grad = True | |
| new_k_weight.requires_grad = True | |
| new_v_weight.requires_grad = True | |
| new_out_proj_weight.requires_grad = True | |
| new_q_bias = torch.cat(new_q_bias).detach() | |
| new_q_bias.requires_grad = True | |
| new_k_bias = torch.cat(new_k_bias).detach() | |
| new_k_bias.requires_grad = True | |
| new_v_bias = torch.cat(new_v_bias).detach() | |
| new_v_bias.requires_grad = True | |
| self.q_proj.weight = nn.Parameter(new_q_weight) | |
| self.q_proj.bias = nn.Parameter(new_q_bias) | |
| self.k_proj.weight = nn.Parameter(new_k_weight) | |
| self.k_proj.bias = nn.Parameter(new_k_bias) | |
| self.v_proj.weight = nn.Parameter(new_v_weight) | |
| self.v_proj.bias = nn.Parameter(new_v_bias) | |
| self.out_proj.weight = nn.Parameter(new_out_proj_weight) | |
| self.num_heads = len(reserve_head_index) | |
| self.embed_dim = self.head_dim * self.num_heads | |
| self.q_proj.out_features = self.embed_dim | |
| self.k_proj.out_features = self.embed_dim | |
| self.v_proj.out_features = self.embed_dim | |
| def _set_skip_embed_dim_check(self): | |
| self.skip_embed_dim_check = True | |
| def _pad_masks(self, key_padding_mask, attn_mask): | |
| if attn_mask is not None: | |
| shape = attn_mask.size()[:-1] + torch.Size([1]) | |
| attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(shape)], dim=-1) | |
| if key_padding_mask is not None: | |
| shape = key_padding_mask.size()[:-1] + torch.Size([1]) | |
| key_padding_mask = torch.cat([key_padding_mask, key_padding_mask.new_zeros(shape)], dim=-1) | |
| return key_padding_mask, attn_mask | |
| def _add_bias(self, k, v, key_padding_mask, attn_mask, bsz): | |
| assert self.bias_k is not None or self.bias_v is not None | |
| key_padding_mask, attn_mask = self._pad_masks(key_padding_mask=key_padding_mask, attn_mask=attn_mask) | |
| return torch.cat([k, self.bias_k.repeat(1, bsz, 1)]), torch.cat([v, self.bias_v.repeat(1, bsz, 1)]), key_padding_mask, attn_mask | |
| def _append_zero_attn(self, k, v, key_padding_mask, attn_mask): | |
| zero_attn_shape = k.size()[:-2] + torch.Size([1]) + k.size()[-1:] | |
| key_padding_mask, attn_mask = self._pad_masks(key_padding_mask=key_padding_mask, attn_mask=attn_mask) | |
| return torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=-2), torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=-2), key_padding_mask, attn_mask | |
| def forward(self, query, key, value, key_padding_mask = None, incremental_state = None, need_weights = True, static_kv = False, attn_mask = None, before_softmax = False, need_head_weights = False): | |
| if need_head_weights: need_weights = True | |
| is_tpu = query.device.type == "xla" | |
| tgt_len, bsz, embed_dim = query.size() | |
| src_len = tgt_len | |
| if not self.skip_embed_dim_check: assert (embed_dim == self.embed_dim) | |
| assert list(query.size()) == [tgt_len, bsz, embed_dim] | |
| if key is not None: | |
| src_len, key_bsz, _ = key.size() | |
| if not torch.jit.is_scripting(): | |
| assert value is not None | |
| assert src_len, key_bsz == value.shape[:2] | |
| if (not self.onnx_trace and not is_tpu and incremental_state is None and not static_kv and not torch.jit.is_scripting() and not self.skip_embed_dim_check): | |
| assert key is not None and value is not None | |
| return F.multi_head_attention_forward(query, key, value, self.embed_dim, self.num_heads, torch.empty([0]), torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)), self.bias_k, self.bias_v, self.add_zero_attn, self.dropout_module.p, self.out_proj.weight, self.out_proj.bias, self.training or self.dropout_module.apply_during_inference, key_padding_mask.bool() if key_padding_mask is not None else None, need_weights, attn_mask, use_separate_proj_weight=True, q_proj_weight=self.q_proj.weight, k_proj_weight=self.k_proj.weight, v_proj_weight=self.v_proj.weight) | |
| if incremental_state is not None: | |
| saved_state = self._get_input_buffer(incremental_state) | |
| if saved_state is not None and "prev_key" in saved_state: | |
| if static_kv: | |
| assert self.encoder_decoder_attention and not self.self_attention | |
| key = value = None | |
| else: saved_state = None | |
| if self.self_attention: | |
| q = self.q_proj(query) | |
| k = self.k_proj(query) | |
| v = self.v_proj(query) | |
| elif self.encoder_decoder_attention: | |
| q = self.q_proj(query) | |
| if key is None: | |
| assert value is None | |
| k = v = None | |
| else: | |
| if self.beam_size > 1 and bsz == key.size(1): | |
| key = key.view(key.size(0), -1, self.beam_size, key.size(2))[:, :, 0, :] | |
| if key_padding_mask is not None: key_padding_mask = key_padding_mask.view(-1, self.beam_size, key_padding_mask.size(1))[:, 0, :] | |
| k = self.k_proj(key) | |
| v = self.v_proj(key) | |
| else: | |
| assert key is not None and value is not None | |
| q = self.q_proj(query) | |
| k = self.k_proj(key) | |
| v = self.v_proj(value) | |
| q *= self.scaling | |
| if self.bias_k is not None: | |
| assert self.bias_v is not None | |
| k, v, attn_mask, key_padding_mask = self._add_bias(k, v, attn_mask, key_padding_mask, bsz) | |
| q = (q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)) | |
| kv_bsz = bsz | |
| if k is not None: | |
| kv_bsz = k.size(1) | |
| k = (k.contiguous().view(-1, kv_bsz * self.num_heads, self.head_dim).transpose(0, 1)) | |
| if v is not None: v = (v.contiguous().view(-1, kv_bsz * self.num_heads, self.head_dim).transpose(0, 1)) | |
| if saved_state is not None: | |
| if "prev_key" in saved_state: | |
| _prev_key = saved_state["prev_key"] | |
| assert _prev_key is not None | |
| kv_bsz = _prev_key.size(0) | |
| prev_key = _prev_key.view(kv_bsz * self.num_heads, -1, self.head_dim) | |
| if static_kv: k = prev_key | |
| else: | |
| assert k is not None | |
| k = torch.cat([prev_key, k], dim=1) | |
| src_len = k.size(1) | |
| if "prev_value" in saved_state: | |
| _prev_value = saved_state["prev_value"] | |
| assert _prev_value is not None or kv_bsz == _prev_value.size(0) | |
| prev_value = _prev_value.view(kv_bsz * self.num_heads, -1, self.head_dim) | |
| if static_kv: v = prev_value | |
| else: | |
| assert v is not None | |
| v = torch.cat([prev_value, v], dim=1) | |
| prev_key_padding_mask = None | |
| if "prev_key_padding_mask" in saved_state: prev_key_padding_mask = saved_state["prev_key_padding_mask"] | |
| assert k is not None and v is not None | |
| key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(key_padding_mask=key_padding_mask, prev_key_padding_mask=prev_key_padding_mask, batch_size=kv_bsz, src_len=k.size(1), static_kv=static_kv) | |
| saved_state["prev_key"] = k.view(kv_bsz, self.num_heads, -1, self.head_dim) | |
| saved_state["prev_value"] = v.view(kv_bsz, self.num_heads, -1, self.head_dim) | |
| saved_state["prev_key_padding_mask"] = key_padding_mask | |
| assert incremental_state is not None | |
| incremental_state = self._set_input_buffer(incremental_state, saved_state) | |
| assert k is not None | |
| assert k.size(1) == src_len | |
| if key_padding_mask is not None and key_padding_mask.dim() == 0: key_padding_mask = None | |
| if key_padding_mask is not None: | |
| assert key_padding_mask.size(0) == kv_bsz | |
| assert key_padding_mask.size(1) == src_len | |
| if self.add_zero_attn: | |
| assert v is not None | |
| src_len += 1 | |
| k, v, key_padding_mask, attn_mask = self._append_zero_attn(k=k, v=v, key_padding_mask=key_padding_mask, attn_mask=attn_mask) | |
| if self.encoder_decoder_attention and bsz != kv_bsz: | |
| attn_weights = torch.einsum("bxhtd,bhsd->bxhts", q.view((kv_bsz, -1, self.num_heads) + q.size()[1:]), k.view((kv_bsz, self.num_heads) + k.size()[1:])) | |
| attn_weights = attn_weights.reshape((-1,) + attn_weights.size()[-2:]) | |
| else: attn_weights = torch.bmm(q, k.transpose(1, 2)) | |
| attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) | |
| assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] | |
| if attn_mask is not None: | |
| attn_mask = attn_mask.unsqueeze(0) | |
| if self.onnx_trace: attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1) | |
| attn_weights += attn_mask | |
| if key_padding_mask is not None: | |
| attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) | |
| attn_weights = attn_weights.view(kv_bsz, -1, self.num_heads, tgt_len, src_len).masked_fill(key_padding_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3).to(torch.bool), float("-inf")) if not is_tpu else attn_weights.transpose(0, 2).masked_fill(key_padding_mask, float("-inf")).transpose(0, 2) | |
| attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) | |
| if before_softmax: return attn_weights, v | |
| attn_weights_float = softmax(attn_weights, dim=-1, onnx_trace=self.onnx_trace) | |
| attn_weights = attn_weights_float.type_as(attn_weights) | |
| attn_probs = self.dropout_module(attn_weights) | |
| assert v is not None | |
| attn = None | |
| if self.encoder_decoder_attention and bsz != kv_bsz: | |
| attn = torch.einsum("bxhts,bhsd->bxhtd", attn_probs.view((kv_bsz, -1, self.num_heads) + attn_probs.size()[1:]), v.view((kv_bsz, self.num_heads) + v.size()[1:])) | |
| attn = attn.reshape((-1,) + attn.size()[-2:]) | |
| else: attn = torch.bmm(attn_probs, v) | |
| assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] | |
| if self.onnx_trace and attn.size(1) == 1: attn = attn.contiguous().view(tgt_len, bsz, self.embed_dim) | |
| else: attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim) | |
| attn = self.out_proj(attn) | |
| attn_weights = None | |
| if need_weights: | |
| attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0) | |
| if not need_head_weights: attn_weights = attn_weights.mean(dim=0) | |
| return attn, attn_weights | |
| def _append_prev_key_padding_mask(key_padding_mask, prev_key_padding_mask, batch_size, src_len, static_kv): | |
| if prev_key_padding_mask is not None and static_kv: new_key_padding_mask = prev_key_padding_mask | |
| elif prev_key_padding_mask is not None and key_padding_mask is not None: new_key_padding_mask = torch.cat([prev_key_padding_mask.float(), key_padding_mask.float()], dim=1) | |
| elif prev_key_padding_mask is not None: | |
| if src_len > prev_key_padding_mask.size(1): | |
| filler = torch.zeros((batch_size, src_len - prev_key_padding_mask.size(1)), device=prev_key_padding_mask.device) | |
| new_key_padding_mask = torch.cat([prev_key_padding_mask.float(), filler.float()], dim=1) | |
| else: new_key_padding_mask = prev_key_padding_mask.float() | |
| elif key_padding_mask is not None: | |
| if src_len > key_padding_mask.size(1): | |
| filler = torch.zeros((batch_size, src_len - key_padding_mask.size(1)), device=key_padding_mask.device) | |
| new_key_padding_mask = torch.cat([filler.float(), key_padding_mask.float()], dim=1) | |
| else: new_key_padding_mask = key_padding_mask.float() | |
| else: new_key_padding_mask = prev_key_padding_mask | |
| return new_key_padding_mask | |
| def reorder_incremental_state(self, incremental_state, new_order): | |
| input_buffer = self._get_input_buffer(incremental_state) | |
| if input_buffer is not None: | |
| for k in input_buffer.keys(): | |
| input_buffer_k = input_buffer[k] | |
| if input_buffer_k is not None: | |
| if self.encoder_decoder_attention: | |
| if input_buffer_k.size(0) * self.beam_size == new_order.size(0): return incremental_state | |
| elif self.beam_size > 1: input_buffer[k] = input_buffer_k.index_select(0, new_order.reshape(-1, self.beam_size)[:, 0] // self.beam_size) | |
| else: input_buffer[k] = input_buffer_k.index_select(0, new_order) | |
| else: input_buffer[k] = input_buffer_k.index_select(0, new_order) | |
| incremental_state = self._set_input_buffer(incremental_state, input_buffer) | |
| return incremental_state | |
| def set_beam_size(self, beam_size): | |
| self.beam_size = beam_size | |
| def _get_input_buffer(self, incremental_state): | |
| result = self.get_incremental_state(incremental_state, "attn_state") | |
| if result is not None: return result | |
| else: return {} | |
| def _set_input_buffer(self, incremental_state, buffer): | |
| return self.set_incremental_state(incremental_state, "attn_state", buffer) | |
| def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int): | |
| return attn_weights | |
| def upgrade_state_dict_named(self, state_dict, name): | |
| prefix = name + "." if name != "" else "" | |
| items_to_add = {} | |
| keys_to_remove = [] | |
| for k in state_dict.keys(): | |
| if k.endswith(prefix + "in_proj_weight"): | |
| dim = int(state_dict[k].shape[0] / 3) | |
| items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim] | |
| items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim] | |
| items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :] | |
| keys_to_remove.append(k) | |
| k_bias = prefix + "in_proj_bias" | |
| if k_bias in state_dict.keys(): | |
| dim = int(state_dict[k].shape[0] / 3) | |
| items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim] | |
| items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][dim : 2 * dim] | |
| items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :] | |
| keys_to_remove.append(prefix + "in_proj_bias") | |
| for k in keys_to_remove: | |
| del state_dict[k] | |
| for key, value in items_to_add.items(): | |
| state_dict[key] = value | |
| def init_bert_params(module): | |
| def normal_(data): | |
| data.copy_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device)) | |
| if isinstance(module, nn.Linear): | |
| normal_(module.weight.data) | |
| if module.bias is not None: module.bias.data.zero_() | |
| if isinstance(module, nn.Embedding): | |
| normal_(module.weight.data) | |
| if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() | |
| if isinstance(module, MultiheadAttention): | |
| normal_(module.q_proj.weight.data) | |
| normal_(module.k_proj.weight.data) | |
| normal_(module.v_proj.weight.data) | |
| def make_conv_pos(e, k, g): | |
| pos_conv = nn.Conv1d(e, e, kernel_size=k, padding=k // 2, groups=g) | |
| dropout = 0 | |
| nn.init.normal_(pos_conv.weight, mean=0, std=math.sqrt((4 * (1.0 - dropout)) / (k * e))) | |
| nn.init.constant_(pos_conv.bias, 0) | |
| return nn.Sequential(nn.utils.parametrizations.weight_norm(pos_conv, name="weight", dim=2), SamePad(k), nn.GELU()) | |
| def is_xla_tensor(tensor): | |
| return torch.is_tensor(tensor) and tensor.device.type == "xla" | |
| def index_put(tensor, indices, value): | |
| if is_xla_tensor(tensor): | |
| for _ in range(indices.dim(), tensor.dim()): | |
| indices = indices.unsqueeze(-1) | |
| if indices.size(-1) < tensor.size(-1): indices = indices.expand_as(tensor) | |
| tensor = torch.mul(tensor, ~indices) + torch.mul(value, indices) | |
| else: tensor[indices] = value | |
| return tensor | |
| def pad_to_multiple(x, multiple, dim=-1, value=0): | |
| if x is None: return None, 0 | |
| tsz = x.size(dim) | |
| m = tsz / multiple | |
| remainder = math.ceil(m) * multiple - tsz | |
| if m.is_integer(): return x, 0 | |
| return F.pad(x, (*((0,) * (-1 - dim) * 2), 0, remainder), value=value), remainder | |
| def compute_mask_indices(shape, padding_mask, mask_prob, mask_length, mask_type = "static", mask_other = 0.0, min_masks = 0, no_overlap = False, min_space = 0, require_same_masks = True, mask_dropout = 0.0, add_masks = False, seed = None, epoch = None, indices = None, idc_select_ver = 1, num_mask_ver = 2): | |
| bsz, all_sz = shape | |
| mask = np.full((bsz, all_sz), False) | |
| if num_mask_ver == 1: all_num_mask = max(min_masks, int(mask_prob * all_sz / float(mask_length) + np.random.rand())) | |
| mask_idcs = [] | |
| for i in range(bsz): | |
| seed_i = int(hash((seed, epoch, indices[i].item())) % 1e6) if seed is not None and epoch is not None and indices is not None else None | |
| rng = np.random.default_rng(seed_i) | |
| if padding_mask is not None: | |
| sz = all_sz - padding_mask[i].long().sum().item() | |
| assert sz >= 0, sz | |
| else: sz = all_sz | |
| if num_mask_ver == 1: num_mask = max(min_masks, int(mask_prob * sz / float(mask_length) + np.random.rand())) if padding_mask is not None else all_num_mask | |
| elif num_mask_ver == 2: num_mask = max(min_masks, int(mask_prob * sz / float(mask_length) + rng.random())) | |
| else: raise ValueError | |
| if mask_type == "static": lengths = np.full(num_mask, mask_length) | |
| elif mask_type == "uniform": lengths = rng.randint(mask_other, mask_length * 2 + 1, size=num_mask) | |
| elif mask_type == "normal": lengths = [max(1, int(round(x))) for x in rng.normal(mask_length, mask_other, size=num_mask)] | |
| elif mask_type == "poisson": lengths = [int(round(x)) for x in rng.poisson(mask_length, size=num_mask)] | |
| else: raise Exception | |
| if sum(lengths) == 0: | |
| if mask_type == "static": raise ValueError | |
| else: lengths = [min(mask_length, sz - 1)] | |
| if no_overlap: | |
| mask_idc = [] | |
| def arrange(s, e, length, keep_length): | |
| span_start = rng.randint(s, e - length) | |
| mask_idc.extend(span_start + i for i in range(length)) | |
| new_parts = [] | |
| if span_start - s - min_space >= keep_length: new_parts.append((s, span_start - min_space + 1)) | |
| if e - span_start - length - min_space > keep_length: new_parts.append((span_start + length + min_space, e)) | |
| return new_parts | |
| parts = [(0, sz)] | |
| min_length = min(lengths) | |
| for length in sorted(lengths, reverse=True): | |
| lens = np.fromiter((e - s if e - s >= length + min_space else 0 for s, e in parts), np.int32) | |
| l_sum = np.sum(lens) | |
| if l_sum == 0: break | |
| s, e = parts.pop(rng.choice(len(parts), p=lens / np.sum(lens))) | |
| parts.extend(arrange(s, e, length, min_length)) | |
| mask_idc = np.asarray(mask_idc) | |
| else: | |
| if idc_select_ver == 1: | |
| min_len = min(lengths) | |
| if sz - min_len <= num_mask: min_len = sz - num_mask - 1 | |
| mask_idc = rng.choice(sz - min_len, num_mask, replace=False) | |
| elif idc_select_ver == 2: mask_idc = rng.choice(sz, num_mask, replace=False) | |
| else: raise ValueError | |
| mask_idc = np.asarray([mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(lengths[j])]) | |
| mask_idc = np.unique(mask_idc[mask_idc < sz]) | |
| if len(mask_idc) >= sz: raise ValueError | |
| mask_idcs.append(mask_idc) | |
| target_len = None | |
| if require_same_masks: target_len = max([len(m) for m in mask_idcs]) if add_masks else min([len(m) for m in mask_idcs]) | |
| for i, mask_idc in enumerate(mask_idcs): | |
| if target_len is not None and len(mask_idc) > target_len: mask_idc = rng.choice(mask_idc, target_len, replace=False) | |
| mask[i, mask_idc] = True | |
| if target_len is not None and len(mask_idc) < target_len: | |
| to_mask = rng.choice(np.flatnonzero(~mask[i]), target_len - len(mask_idc), replace=False) | |
| mask[i, to_mask] = True | |
| if mask_dropout > 0: | |
| masked = np.flatnonzero(mask[i]) | |
| mask[i, rng.choice(masked, np.rint(len(masked) * mask_dropout).astype(int), replace=False)] = False | |
| return mask | |
| def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True): | |
| return nn.LayerNorm(normalized_shape, eps, elementwise_affine) | |
| def prune_state_dict(state_dict, model_cfg): | |
| arch = None | |
| if model_cfg is not None: arch = (model_cfg._name if isinstance(model_cfg, DictConfig) else getattr(model_cfg, "arch", None)) | |
| if not model_cfg or arch is None or arch == "ptt_transformer": return state_dict | |
| encoder_layers_to_keep = getattr(model_cfg, "encoder_layers_to_keep", None) | |
| decoder_layers_to_keep = getattr(model_cfg, "decoder_layers_to_keep", None) | |
| if not encoder_layers_to_keep and not decoder_layers_to_keep: return state_dict | |
| def create_pruning_pass(layers_to_keep, layer_name): | |
| keep_layers = sorted(int(layer_string) for layer_string in layers_to_keep.split(",")) | |
| mapping_dict = {} | |
| for i in range(len(keep_layers)): | |
| mapping_dict[str(keep_layers[i])] = str(i) | |
| return {"substitution_regex": re.compile(r"^{layer}.*\.layers\.(\d+)".format(layer=layer_name)), "mapping_dict": mapping_dict} | |
| pruning_passes = [] | |
| new_state_dict = {} | |
| if encoder_layers_to_keep: pruning_passes.append(create_pruning_pass(encoder_layers_to_keep, "encoder")) | |
| if decoder_layers_to_keep: pruning_passes.append(create_pruning_pass(decoder_layers_to_keep, "decoder")) | |
| for layer_name in state_dict.keys(): | |
| match = re.search(r"\.layers\.(\d+)\.", layer_name) | |
| if not match: | |
| new_state_dict[layer_name] = state_dict[layer_name] | |
| continue | |
| original_layer_number = match.group(1) | |
| for pruning_pass in pruning_passes: | |
| if original_layer_number in pruning_pass["mapping_dict"] and pruning_pass["substitution_regex"].search(layer_name): | |
| substitution_match = pruning_pass["substitution_regex"].search(layer_name) | |
| new_state_dict[(layer_name[: substitution_match.start(1)] + pruning_pass["mapping_dict"][original_layer_number] + layer_name[substitution_match.end(1) :])] = state_dict[layer_name] | |
| with open_dict(model_cfg) if isinstance(model_cfg, DictConfig) else contextlib.ExitStack(): | |
| if hasattr(model_cfg, "encoder_layers_to_keep"): model_cfg.encoder_layers_to_keep = None | |
| if hasattr(model_cfg, "decoder_layers_to_keep"): model_cfg.decoder_layers_to_keep = None | |
| return new_state_dict | |
| def relu_squared(x): | |
| return F.relu(x).pow(2) | |
| def get_activation_fn(activation): | |
| def gelu(x): | |
| return nn.functional.gelu(x.float()).type_as(x) | |
| def gelu_accurate(x): | |
| if not hasattr(gelu_accurate, "_a"): | |
| gelu_accurate._a = math.sqrt(2 / math.pi) | |
| return (0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3))))) | |
| if activation == "relu": return F.relu | |
| elif activation == "relu_squared": return relu_squared | |
| elif activation == "gelu": return gelu | |
| elif activation == "gelu_fast": return gelu_accurate | |
| elif activation == "gelu_accurate": return gelu_accurate | |
| elif activation == "tanh": return torch.tanh | |
| elif activation == "linear": return lambda x: x | |
| elif activation == "swish": return nn.SiLU | |
| else: raise RuntimeError | |
| class SamePad(nn.Module): | |
| def __init__(self, kernel_size, causal=False): | |
| super().__init__() | |
| if causal: self.remove = kernel_size - 1 | |
| else: self.remove = 1 if kernel_size % 2 == 0 else 0 | |
| def forward(self, x): | |
| if self.remove > 0: x = x[:, :, : -self.remove] | |
| return x | |
| class TransformerSentenceEncoderLayer(nn.Module): | |
| def __init__(self, embedding_dim = 768, ffn_embedding_dim = 3072, num_attention_heads = 8, dropout = 0.1, attention_dropout = 0.1, activation_dropout = 0.1, activation_fn = "relu", layer_norm_first = False): | |
| super().__init__() | |
| self.embedding_dim = embedding_dim | |
| self.dropout = dropout | |
| self.activation_dropout = activation_dropout | |
| self.activation_fn = get_activation_fn(activation_fn) | |
| self.self_attn = MultiheadAttention(self.embedding_dim, num_attention_heads, dropout=attention_dropout, self_attention=True) | |
| self.dropout1 = nn.Dropout(dropout) | |
| self.dropout2 = nn.Dropout(self.activation_dropout) | |
| self.dropout3 = nn.Dropout(dropout) | |
| self.layer_norm_first = layer_norm_first | |
| self.self_attn_layer_norm = LayerNorm(self.embedding_dim) | |
| self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim) | |
| self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim) | |
| self.final_layer_norm = LayerNorm(self.embedding_dim) | |
| def forward(self, x, self_attn_mask=None, self_attn_padding_mask=None, need_weights=False, att_args=None): | |
| residual = x | |
| if self.layer_norm_first: | |
| x = self.self_attn_layer_norm(x) | |
| x, attn = self.self_attn(query=x, key=x, value=x, key_padding_mask=self_attn_padding_mask, attn_mask=self_attn_mask, need_weights=False) | |
| x = residual + self.dropout1(x) | |
| residual = x | |
| x = self.fc2(self.dropout2(self.activation_fn(self.fc1(self.final_layer_norm(x))))) | |
| layer_result = x | |
| x = residual + self.dropout3(x) | |
| else: | |
| x, attn = self.self_attn(query=x, key=x, value=x, key_padding_mask=self_attn_padding_mask, need_weights=False) | |
| x = self.self_attn_layer_norm(residual + self.dropout1(x)) | |
| residual = x | |
| x = self.fc2(self.dropout2(self.activation_fn(self.fc1(x)))) | |
| layer_result = x | |
| x = self.final_layer_norm(residual + self.dropout3(x)) | |
| return x, (attn, layer_result) | |
| class AdapterFast(nn.Module): | |
| def __init__(self, adapter_num, input_dim, hidden_dim, act_fn): | |
| super().__init__() | |
| self.adapter_num = adapter_num | |
| self.input_dim = input_dim | |
| self.hidden_dim = hidden_dim | |
| self.W_a = nn.Parameter(torch.empty(adapter_num, hidden_dim, input_dim)) | |
| self.W_b = nn.Parameter(torch.empty(adapter_num, input_dim, hidden_dim)) | |
| self.b_a = nn.Parameter(torch.empty(adapter_num, hidden_dim)) | |
| self.b_b = nn.Parameter(torch.empty(adapter_num, input_dim)) | |
| self.ln_W = nn.Parameter(torch.empty(adapter_num, input_dim)) | |
| self.ln_b = nn.Parameter(torch.empty(adapter_num, input_dim)) | |
| self.act_fn = nn.Identity() | |
| if act_fn == "relu": self.act_fn = nn.ReLU() | |
| elif act_fn == "gelu": self.act_fn = nn.GELU() | |
| elif act_fn == "selu": self.act_fn = nn.SELU() | |
| else: raise ValueError | |
| self.input_dim = input_dim | |
| self.reset_parameters() | |
| def reset_parameters(self): | |
| for ii in range(self.adapter_num): | |
| nn.init.kaiming_uniform_(self.W_a[ii], a=math.sqrt(5)) | |
| nn.init.kaiming_uniform_(self.W_b[ii], a=math.sqrt(5)) | |
| fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.W_a[ii]) | |
| bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 | |
| nn.init.uniform_(self.b_a[ii], -bound, bound) | |
| fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.W_b[ii]) | |
| bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 | |
| nn.init.uniform_(self.b_b[ii], -bound, bound) | |
| nn.init.ones_(self.ln_W) | |
| nn.init.zeros_(self.ln_b) | |
| def forward(self, x, adapter_id): | |
| ii = adapter_id | |
| return F.linear(self.act_fn(F.linear(F.layer_norm(x, (self.input_dim, ), self.ln_W[ii], self.ln_b[ii]), self.W_a[ii], self.b_a[ii])), self.W_b[ii], self.b_b[ii]) | |
| def extra_repr(self): | |
| return ('adapter={}, input_dim={}, hidden_dim={}'.format(self.adapter_num, self.input_dim, self.hidden_dim)) | |
| class FeedForwardModule(nn.Module): | |
| def __init__(self, input_feat, hidden_units, dropout1, dropout2, activation_fn="swish", bias=True): | |
| super(FeedForwardModule, self).__init__() | |
| self.layer_norm = LayerNorm(input_feat) | |
| self.w_1 = nn.Linear(input_feat, hidden_units, bias=bias) | |
| self.w_2 = nn.Linear(hidden_units, input_feat, bias=bias) | |
| self.dropout1 = nn.Dropout(dropout1) | |
| self.dropout2 = nn.Dropout(dropout2) | |
| self.activation = get_activation_fn(activation_fn)(hidden_units) | |
| def forward(self, x): | |
| return self.dropout2(self.w_2(self.dropout1(self.activation(self.w_1(self.layer_norm(x)))))) | |
| class ConvolutionModule(nn.Module): | |
| def __init__(self, embed_dim, channels, depthwise_kernel_size, dropout, activation_fn="swish", bias=False, export=False): | |
| super(ConvolutionModule, self).__init__() | |
| assert (depthwise_kernel_size - 1) % 2 == 0 | |
| self.layer_norm = LayerNorm(embed_dim, export=export) | |
| self.pointwise_conv1 = nn.Conv1d(embed_dim, 2 * channels, kernel_size=1, stride=1, padding=0, bias=bias) | |
| self.glu = nn.GLU(dim=1) | |
| self.depthwise_conv = nn.Conv1d(channels, channels, depthwise_kernel_size, stride=1, padding=(depthwise_kernel_size - 1) // 2, groups=channels, bias=bias) | |
| self.batch_norm = nn.BatchNorm1d(channels) | |
| self.activation = get_activation_fn(activation_fn)(channels) | |
| self.pointwise_conv2 = nn.Conv1d(channels, embed_dim, kernel_size=1, stride=1, padding=0, bias=bias) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| return self.dropout(self.pointwise_conv2(self.activation(self.batch_norm(self.depthwise_conv(self.glu(self.pointwise_conv1(self.layer_norm(x).transpose(1, 2)))))))).transpose(1, 2) | |
| def rotate_half(x): | |
| x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :] | |
| return torch.cat((-x2, x1), dim=x1.ndim - 1) | |
| def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0): | |
| cos, sin = (cos[offset : q.shape[0] + offset, ...], sin[offset : q.shape[0] + offset, ...]) | |
| return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin) | |
| class RotaryPositionalEmbedding(nn.Module): | |
| def __init__(self, dim, base=10000, precision=torch.half): | |
| super().__init__() | |
| inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) | |
| self.register_buffer("inv_freq", inv_freq) | |
| self.seq_len_cached = 0 | |
| self.cos_cached = torch.empty(self.seq_len_cached, 1, 1, dim) | |
| self.sin_cached = torch.empty(self.seq_len_cached, 1, 1, dim) | |
| self.precision = precision | |
| def forward(self, x, seq_len = 0): | |
| if seq_len > self.seq_len_cached: | |
| self.seq_len_cached = seq_len | |
| freqs = torch.einsum("i,j->ij", torch.arange(seq_len, device=x.device).type_as(self.inv_freq), self.inv_freq) | |
| emb = torch.cat((freqs, freqs), dim=-1).to(x.device) | |
| self.cos_cached = emb.cos().view(emb.size(0), 1, 1, emb.size(1)) | |
| self.sin_cached = emb.sin().view(emb.size(0), 1, 1, emb.size(1)) | |
| return self.cos_cached, self.sin_cached | |
| class ESPNETMultiHeadedAttention(nn.Module): | |
| def __init__(self, n_feat, n_head, dropout): | |
| super(ESPNETMultiHeadedAttention, self).__init__() | |
| assert n_feat % n_head == 0 | |
| self.d_k = n_feat // n_head | |
| self.h = n_head | |
| self.linear_q = nn.Linear(n_feat, n_feat) | |
| self.linear_k = nn.Linear(n_feat, n_feat) | |
| self.linear_v = nn.Linear(n_feat, n_feat) | |
| self.linear_out = nn.Linear(n_feat, n_feat) | |
| self.attn = None | |
| self.dropout = nn.Dropout(p=dropout) | |
| def forward_qkv(self, query, key, value, **kwargs): | |
| n_batch = query.size(0) | |
| return self.linear_q(query).view(n_batch, -1, self.h, self.d_k).transpose(1, 2), self.linear_k(key).view(n_batch, -1, self.h, self.d_k).transpose(1, 2), self.linear_v(value).view(n_batch, -1, self.h, self.d_k).transpose(1, 2) | |
| def forward_attention(self, value, scores, mask): | |
| n_batch = value.size(0) | |
| if mask is not None: | |
| scores = scores.masked_fill(mask.unsqueeze(1).unsqueeze(2).to(bool), float("-inf")) | |
| self.attn = torch.softmax(scores, dim=-1) | |
| else: self.attn = torch.softmax(scores, dim=-1) | |
| return self.linear_out((torch.matmul(self.dropout(self.attn), value).transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k))) | |
| def forward(self, query, key, value, key_padding_mask=None, **kwargs): | |
| q, k, v = self.forward_qkv(query.transpose(0, 1), key.transpose(0, 1), value.transpose(0, 1)) | |
| return self.forward_attention(v, torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k), key_padding_mask).transpose(0, 1), None | |
| class RelPositionMultiHeadedAttention(ESPNETMultiHeadedAttention): | |
| def __init__(self, n_feat, n_head, dropout, zero_triu=False): | |
| super().__init__(n_feat, n_head, dropout) | |
| self.zero_triu = zero_triu | |
| self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) | |
| self.pos_bias_u = nn.Parameter(torch.zeros(self.h, self.d_k)) | |
| self.pos_bias_v = nn.Parameter(torch.zeros(self.h, self.d_k)) | |
| nn.init.xavier_uniform_(self.pos_bias_u) | |
| nn.init.xavier_uniform_(self.pos_bias_v) | |
| def rel_shift(self, x): | |
| x = torch.cat([torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype), x], dim=-1).view(*x.size()[:2], x.size(3) + 1, x.size(2))[:, :, 1:].view_as(x)[:, :, :, : x.size(-1) // 2 + 1] | |
| if self.zero_triu: x = x * torch.tril(torch.ones((x.size(2), x.size(3)), device=x.device), x.size(3) - x.size(2))[None, None, :, :] | |
| return x | |
| def forward(self, query, key, value, pos_emb, key_padding_mask=None, **kwargs): | |
| pos_emb = pos_emb.transpose(0, 1) | |
| q, k, v = self.forward_qkv(query.transpose(0, 1), key.transpose(0, 1), value.transpose(0, 1)) | |
| q = q.transpose(1, 2) | |
| return self.forward_attention(v, (torch.matmul((q + self.pos_bias_u).transpose(1, 2), k.transpose(-2, -1)) + self.rel_shift(torch.matmul((q + self.pos_bias_v).transpose(1, 2), self.linear_pos(pos_emb).view(pos_emb.size(0), -1, self.h, self.d_k).transpose(1, 2).transpose(-2, -1)))) / math.sqrt(self.d_k), key_padding_mask).transpose(0, 1), None | |
| class RotaryPositionMultiHeadedAttention(ESPNETMultiHeadedAttention): | |
| def __init__(self, n_feat, n_head, dropout, precision, rotary_emd_base=10000): | |
| super().__init__(n_feat, n_head, dropout) | |
| precision = torch.float | |
| self.rotary_ndims = self.d_k | |
| if precision == "fp16": precision = torch.half | |
| self.rotary_emb = RotaryPositionalEmbedding(self.rotary_ndims, base=rotary_emd_base, precision=precision) | |
| def forward(self, query, key, value, key_padding_mask=None, **kwargs): | |
| T, B, C = value.size() | |
| query = query.view(T, B, self.h, self.d_k) | |
| key = key.view(T, B, self.h, self.d_k) | |
| value = value.view(T, B, self.h, self.d_k) | |
| cos, sin = self.rotary_emb(value, seq_len=T) | |
| query, key = apply_rotary_pos_emb(query, key, cos, sin, offset=0) | |
| query = query.view(T, B, self.h * self.d_k) | |
| key = key.view(T, B, self.h * self.d_k) | |
| value = value.view(T, B, self.h * self.d_k) | |
| q, k, v = self.forward_qkv(query.transpose(0, 1), key.transpose(0, 1), value.transpose(0, 1)) | |
| return self.forward_attention(v, torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k), key_padding_mask).transpose(0, 1), None | |
| class ConformerEncoderLayer(nn.Module): | |
| def __init__(self, embed_dim, ffn_embed_dim, attention_heads, dropout, use_fp16, depthwise_conv_kernel_size=31, activation_fn="swish", attn_type=None, pos_enc_type="abs"): | |
| self.pos_enc_type = pos_enc_type | |
| super(ConformerEncoderLayer, self).__init__() | |
| self.ffn1 = FeedForwardModule(embed_dim, ffn_embed_dim, dropout, dropout) | |
| self.self_attn_layer_norm = LayerNorm(embed_dim, export=False) | |
| self.self_attn_dropout = nn.Dropout(dropout) | |
| if attn_type == "espnet": | |
| if self.pos_enc_type == "rel_pos": self.self_attn = RelPositionMultiHeadedAttention(embed_dim, attention_heads, dropout=dropout) | |
| elif self.pos_enc_type == "rope": self.self_attn = RotaryPositionMultiHeadedAttention(embed_dim, attention_heads, dropout=dropout, precision=use_fp16) | |
| elif self.pos_enc_type == "abs": self.self_attn = ESPNETMultiHeadedAttention(embed_dim, attention_heads, dropout=dropout) | |
| else: raise Exception | |
| else: self.self_attn = MultiheadAttention(embed_dim, attention_heads, dropout=dropout) | |
| self.conv_module = ConvolutionModule(embed_dim=embed_dim, channels=embed_dim, depthwise_kernel_size=depthwise_conv_kernel_size, dropout=dropout, activation_fn=activation_fn) | |
| self.ffn2 = FeedForwardModule(embed_dim, ffn_embed_dim, dropout, dropout, activation_fn=activation_fn) | |
| self.final_layer_norm = LayerNorm(embed_dim, export=False) | |
| def forward(self, x, encoder_padding_mask, position_emb = None): | |
| residual = x | |
| x = self.ffn1(x) * 0.5 + residual | |
| residual = x | |
| x = self.self_attn_layer_norm(x) | |
| if self.pos_enc_type == "rel_pos": x, attn = self.self_attn(query=x, key=x, value=x, key_padding_mask=encoder_padding_mask, pos_emb=position_emb, need_weights=False) | |
| else: x, attn = self.self_attn(query=x, key=x, value=x, key_padding_mask=encoder_padding_mask, need_weights=False) | |
| x = self.self_attn_dropout(x) | |
| x = x + residual | |
| residual = x | |
| x = residual + self.conv_module(x.transpose(0, 1)).transpose(0, 1) | |
| residual = x | |
| x = self.ffn2(x) | |
| layer_result = x | |
| x = self.final_layer_norm(x * 0.5 + residual) | |
| return x, (attn, layer_result) | |
| class ConformerWav2Vec2EncoderLayer(ConformerEncoderLayer): | |
| def forward(self, x, self_attn_mask=None, self_attn_padding_mask=None, need_weights=False, att_args=None, position_emb=None): | |
| return super().forward(x, self_attn_padding_mask, position_emb) | |
| class TransformerSentenceEncoderWithAdapterLayer(TransformerSentenceEncoderLayer): | |
| def __init__(self, embedding_dim = 768, ffn_embedding_dim = 3072, num_attention_heads = 8, dropout = 0.1, attention_dropout = 0.1, activation_dropout = 0.1, activation_fn = "relu", layer_norm_first = False, adapter_num=201, adapter_dim=64, adapter_act_fn="relu"): | |
| super().__init__(embedding_dim=embedding_dim, ffn_embedding_dim=ffn_embedding_dim, num_attention_heads=num_attention_heads, dropout=dropout, attention_dropout=attention_dropout, activation_dropout=activation_dropout, activation_fn=activation_fn, layer_norm_first=layer_norm_first) | |
| self.adapter_num = adapter_num | |
| self.adapter_dim = adapter_dim | |
| self.adapter_layer = AdapterFast(adapter_num, self.embedding_dim, self.adapter_dim, adapter_act_fn) | |
| def forward(self, x, self_attn_mask=None, self_attn_padding_mask=None, need_weights=False, att_args=None, corpus_key=None): | |
| x, (attn, layer_result) = super().forward(x=x, self_attn_mask=self_attn_mask, self_attn_padding_mask=self_attn_padding_mask, need_weights=need_weights, att_args=att_args) | |
| assert corpus_key is not None | |
| assert len(set(corpus_key)) == 1 | |
| return x + self.adapter_layer(x, corpus_key[0]), (attn, layer_result) | |
| class TransposeLast(nn.Module): | |
| def __init__(self, deconstruct_idx=None, tranpose_dim=-2): | |
| super().__init__() | |
| self.deconstruct_idx = deconstruct_idx | |
| self.tranpose_dim = tranpose_dim | |
| def forward(self, x): | |
| if self.deconstruct_idx is not None: x = x[self.deconstruct_idx] | |
| return x.transpose(self.tranpose_dim, -1) | |
| class TransformerEncoder(nn.Module): | |
| def build_encoder_layer(self, args, **kwargs): | |
| if args.layer_type == "transformer": layer = TransformerSentenceEncoderLayer(embedding_dim=self.embedding_dim, ffn_embedding_dim=args.encoder_ffn_embed_dim, num_attention_heads=args.encoder_attention_heads, dropout=self.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_fn=args.activation_fn, layer_norm_first=args.layer_norm_first) | |
| elif args.layer_type == "conformer": layer = ConformerWav2Vec2EncoderLayer(embed_dim=self.embedding_dim, ffn_embed_dim=args.encoder_ffn_embed_dim, attention_heads=args.encoder_attention_heads, dropout=args.dropout, depthwise_conv_kernel_size=args.depthwise_conv_kernel_size, activation_fn="swish", attn_type=args.attn_type, use_fp16=args.fp16, pos_enc_type="abs") | |
| elif args.layer_type == "trf_adp": | |
| use_adp = False | |
| if args.adp_trf_idx == "all": use_adp = True | |
| else: | |
| if kwargs.get("layer_idx", None) in list(range(*[int(g) for g in args.adp_trf_idx.split(":")])): use_adp = True | |
| layer = TransformerSentenceEncoderWithAdapterLayer(embedding_dim=self.embedding_dim, ffn_embedding_dim=args.encoder_ffn_embed_dim, num_attention_heads=args.encoder_attention_heads, dropout=self.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_fn=args.activation_fn, layer_norm_first=args.layer_norm_first, adapter_num=args.adp_num, adapter_dim=args.adp_dim, adapter_act_fn=args.adp_act_fn) if use_adp else TransformerSentenceEncoderLayer(embedding_dim=self.embedding_dim, ffn_embedding_dim=args.encoder_ffn_embed_dim, num_attention_heads=args.encoder_attention_heads, dropout=self.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_fn=args.activation_fn, layer_norm_first=args.layer_norm_first,) | |
| return layer | |
| def __init__(self, args): | |
| super().__init__() | |
| self.dropout = args.dropout | |
| self.embedding_dim = args.encoder_embed_dim | |
| self.required_seq_len_multiple = args.required_seq_len_multiple | |
| pos_conv_depth = getattr(args, "pos_conv_depth", 1) | |
| if pos_conv_depth > 1: | |
| num_layers = args.pos_conv_depth | |
| k = max(3, args.conv_pos // num_layers) | |
| def make_conv_block(e, k, g, l): | |
| return nn.Sequential(*[nn.Sequential(nn.Conv1d(e, e, kernel_size=k, padding=k // 2, groups=g), SamePad(k), TransposeLast(), LayerNorm(e, elementwise_affine=False), TransposeLast(), nn.GELU()) for _ in range(l)]) | |
| self.pos_conv = make_conv_block(self.embedding_dim, k, args.conv_pos_groups, num_layers) | |
| else: self.pos_conv = make_conv_pos(self.embedding_dim, args.conv_pos, args.conv_pos_groups) | |
| self.layers = nn.ModuleList([self.build_encoder_layer(args, layer_idx=ii) for ii in range(args.encoder_layers)]) | |
| self.layer_norm_first = args.layer_norm_first | |
| self.layer_norm = LayerNorm(self.embedding_dim) | |
| self.layerdrop = args.encoder_layerdrop | |
| self.apply(init_bert_params) | |
| def forward(self, x, padding_mask=None, layer=None, corpus_key=None): | |
| x, layer_results = self.extract_features(x, padding_mask, layer, corpus_key=corpus_key) | |
| if self.layer_norm_first and layer is None: x = self.layer_norm(x) | |
| return x, layer_results | |
| def extract_features(self, x, padding_mask=None, tgt_layer=None, min_layer=0, corpus_key=None): | |
| if padding_mask is not None: x = index_put(x, padding_mask, 0) | |
| x = x + self.pos_conv(x.transpose(1, 2)).transpose(1, 2) | |
| if not self.layer_norm_first: x = self.layer_norm(x) | |
| x, pad_length = pad_to_multiple(x, self.required_seq_len_multiple, dim=-2, value=0) | |
| if pad_length > 0 and padding_mask is None: | |
| padding_mask = x.new_zeros((x.size(0), x.size(1)), dtype=torch.bool) | |
| padding_mask[:, -pad_length:] = True | |
| else: padding_mask, _ = pad_to_multiple(padding_mask, self.required_seq_len_multiple, dim=-1, value=True) | |
| x = F.dropout(x, p=self.dropout, training=self.training).transpose(0, 1) | |
| layer_results = [] | |
| r = None | |
| for i, layer in enumerate(self.layers): | |
| dropout_probability = np.random.random() if self.layerdrop > 0 else 1 | |
| if not self.training or (dropout_probability > self.layerdrop): | |
| layer_check = layer | |
| if (corpus_key is None) or (not isinstance(layer_check, (TransformerSentenceEncoderWithAdapterLayer))): x, (z, lr) = layer(x, self_attn_padding_mask=padding_mask, need_weights=False) | |
| else: x, (z, lr) = layer(x, self_attn_padding_mask=padding_mask, need_weights=False, corpus_key=corpus_key) | |
| if i >= min_layer: layer_results.append((x, z, lr)) | |
| if i == tgt_layer: | |
| r = x | |
| break | |
| if r is not None: x = r | |
| x = x.transpose(0, 1) | |
| if pad_length > 0: | |
| x = x[:, :-pad_length] | |
| def undo_pad(a, b, c): | |
| return (a[:-pad_length], b[:-pad_length] if b is not None else b, c[:-pad_length]) | |
| layer_results = [undo_pad(*u) for u in layer_results] | |
| return x, layer_results | |
| def max_positions(self): | |
| return self.args.max_positions | |
| def upgrade_state_dict_named(self, state_dict, name): | |
| return state_dict | |
| class Fp32GroupNorm(nn.GroupNorm): | |
| def __init__(self, *args, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| def forward(self, input): | |
| output = F.group_norm(input.float(), self.num_groups, self.weight.float() if self.weight is not None else None, self.bias.float() if self.bias is not None else None, self.eps) | |
| return output.type_as(input) | |
| class Fp32LayerNorm(nn.LayerNorm): | |
| def __init__(self, *args, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| def forward(self, input): | |
| output = F.layer_norm(input.float(), self.normalized_shape, self.weight.float() if self.weight is not None else None, self.bias.float() if self.bias is not None else None, self.eps) | |
| return output.type_as(input) | |
| class ConvFeatureExtractionModel(nn.Module): | |
| def __init__(self, conv_layers, dropout = 0.0, mode = "default", conv_bias = False): | |
| super().__init__() | |
| assert mode in {"default", "layer_norm"} | |
| def block(n_in, n_out, k, stride, is_layer_norm=False, is_group_norm=False, conv_bias=False): | |
| def make_conv(): | |
| conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias) | |
| nn.init.kaiming_normal_(conv.weight) | |
| return conv | |
| assert (is_layer_norm and is_group_norm) == False | |
| if is_layer_norm: return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.Sequential(TransposeLast(), Fp32LayerNorm(dim, elementwise_affine=True), TransposeLast()), nn.GELU()) | |
| elif is_group_norm: return nn.Sequential(make_conv(), nn.Dropout(p=dropout), Fp32GroupNorm(dim, dim, affine=True), nn.GELU()) | |
| else: return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU()) | |
| in_d = 1 | |
| self.conv_layers = nn.ModuleList() | |
| for i, cl in enumerate(conv_layers): | |
| assert len(cl) == 3 | |
| (dim, k, stride) = cl | |
| self.conv_layers.append(block(in_d, dim, k, stride, is_layer_norm=mode == "layer_norm", is_group_norm=mode == "default" and i == 0, conv_bias=conv_bias)) | |
| in_d = dim | |
| def forward(self, x): | |
| x = x.unsqueeze(1) | |
| for conv in self.conv_layers: | |
| x = conv(x) | |
| return x | |
| class GradMultiply(torch.autograd.Function): | |
| def forward(ctx, x, scale): | |
| ctx.scale = scale | |
| res = x.new(x) | |
| return res | |
| def backward(ctx, grad): | |
| return grad * ctx.scale, None | |
| class BaseFairseqModel(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self._is_generation_fast = False | |
| def get_targets(self, sample, net_output): | |
| return sample["target"] | |
| def extract_features(self, *args, **kwargs): | |
| return self(*args, **kwargs) | |
| def load_state_dict(self, state_dict, strict=True, model_cfg = None, args = None): | |
| self.upgrade_state_dict(state_dict) | |
| new_state_dict = prune_state_dict(state_dict, model_cfg) | |
| return super().load_state_dict(new_state_dict, strict) | |
| def upgrade_state_dict(self, state_dict): | |
| self.upgrade_state_dict_named(state_dict, "") | |
| def upgrade_state_dict_named(self, state_dict, name): | |
| assert state_dict is not None | |
| def do_upgrade(m, prefix): | |
| if len(prefix) > 0: prefix += "." | |
| for n, c in m.named_children(): | |
| name = prefix + n | |
| if hasattr(c, "upgrade_state_dict_named"): c.upgrade_state_dict_named(state_dict, name) | |
| elif hasattr(c, "upgrade_state_dict"): c.upgrade_state_dict(state_dict) | |
| do_upgrade(c, name) | |
| do_upgrade(self, name) | |
| def make_generation_fast_(self, **kwargs): | |
| if self._is_generation_fast: return | |
| self._is_generation_fast = True | |
| def apply_remove_weight_norm(module): | |
| try: | |
| nn.utils.remove_weight_norm(module) | |
| except (AttributeError, ValueError): | |
| return | |
| self.apply(apply_remove_weight_norm) | |
| def apply_make_generation_fast_(module, prefix): | |
| if len(prefix) > 0: prefix += "." | |
| base_func = BaseFairseqModel.make_generation_fast_ | |
| for n, m in module.named_modules(): | |
| if (m != self and hasattr(m, "make_generation_fast_") and m.make_generation_fast_.__func__ is not base_func): m.make_generation_fast_(name=prefix + n, **kwargs) | |
| apply_make_generation_fast_(self, "") | |
| self.eval() | |
| class HubertConfig: | |
| def __init__(self, _name, label_rate, encoder_layers_1, logit_temp_ctr, num_negatives, cross_sample_negatives, ctr_layers, extractor_mode = "default", encoder_layers = 12, encoder_embed_dim = 768, encoder_ffn_embed_dim = 3072, encoder_attention_heads = 12, activation_fn = "gelu", layer_type = "transformer", dropout = 0.1, attention_dropout = 0.1, activation_dropout = 0.0, encoder_layerdrop = 0.0, dropout_input = 0.0, dropout_features = 0.0, final_dim = 0, untie_final_proj = False, layer_norm_first = False, conv_feature_layers = "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2", conv_bias = False, logit_temp = 0.1, target_glu = False, feature_grad_mult = 1.0, mask_length = 10, mask_prob = 0.65, mask_selection = "static", mask_other = 0.0, no_mask_overlap = False, mask_min_space = 1, mask_channel_length = 10, mask_channel_prob = 0.0, mask_channel_selection = "static", mask_channel_other = 0.0, no_mask_channel_overlap = False, mask_channel_min_space = 1, conv_pos = 128, conv_pos_groups = 16, conv_pos_batch_norm = False, latent_temp = (2, 0.5, 0.999995), skip_masked = False, skip_nomask = False, checkpoint_activations = False, required_seq_len_multiple = 2, depthwise_conv_kernel_size = 31, attn_type = "", pos_enc_type = "abs", fp16 = False): | |
| self._name = _name | |
| self.label_rate = label_rate | |
| self.encoder_layers_1 = encoder_layers_1 | |
| self.logit_temp_ctr = logit_temp_ctr | |
| self.num_negatives = num_negatives | |
| self.cross_sample_negatives = cross_sample_negatives | |
| self.ctr_layers = ctr_layers | |
| self.extractor_mode = extractor_mode | |
| self.encoder_layers = encoder_layers | |
| self.encoder_embed_dim = encoder_embed_dim | |
| self.encoder_ffn_embed_dim = encoder_ffn_embed_dim | |
| self.encoder_attention_heads = encoder_attention_heads | |
| self.activation_fn = activation_fn | |
| self.layer_type = layer_type | |
| self.dropout = dropout | |
| self.attention_dropout = attention_dropout | |
| self.activation_dropout = activation_dropout | |
| self.encoder_layerdrop = encoder_layerdrop | |
| self.dropout_input = encoder_layerdrop | |
| self.dropout_features = dropout_features | |
| self.final_dim = final_dim | |
| self.untie_final_proj = untie_final_proj | |
| self.layer_norm_first = layer_norm_first | |
| self.conv_feature_layers = conv_feature_layers | |
| self.conv_bias = conv_bias | |
| self.logit_temp = logit_temp | |
| self.target_glu = target_glu | |
| self.feature_grad_mult = feature_grad_mult | |
| self.mask_length = mask_length | |
| self.mask_prob = mask_prob | |
| self.mask_selection = mask_selection | |
| self.mask_other = mask_other | |
| self.no_mask_overlap = no_mask_overlap | |
| self.mask_min_space = mask_min_space | |
| self.mask_channel_length = mask_channel_length | |
| self.mask_channel_prob = mask_channel_prob | |
| self.mask_channel_selection = mask_channel_selection | |
| self.mask_channel_other = mask_channel_other | |
| self.no_mask_channel_overlap = no_mask_channel_overlap | |
| self.mask_channel_min_space = mask_channel_min_space | |
| self.conv_pos = conv_pos | |
| self.conv_pos_groups = conv_pos_groups | |
| self.conv_pos_batch_norm = conv_pos_batch_norm | |
| self.latent_temp = latent_temp | |
| self.skip_masked = skip_masked | |
| self.skip_nomask = skip_nomask | |
| self.checkpoint_activations = checkpoint_activations | |
| self.required_seq_len_multiple = required_seq_len_multiple | |
| self.depthwise_conv_kernel_size = depthwise_conv_kernel_size | |
| self.attn_type = attn_type | |
| self.pos_enc_type = pos_enc_type | |
| self.fp16 = fp16 | |
| class Model_Config(dict): | |
| def __getattr__(*args): | |
| val = dict.get(*args) | |
| return Model_Config(val) if type(val) is dict else val | |
| __setattr__ = dict.__setitem__ | |
| __delattr__ = dict.__delitem__ | |
| class HubertModel(BaseFairseqModel): | |
| def __init__(self, cfg): | |
| super().__init__() | |
| feature_enc_layers = eval(cfg.conv_feature_layers) | |
| self.embed = feature_enc_layers[-1][0] | |
| self.feature_extractor = ConvFeatureExtractionModel(conv_layers=feature_enc_layers, dropout=0.0, mode=cfg.extractor_mode, conv_bias=cfg.conv_bias) | |
| feature_ds_rate = np.prod([s for _, _, s in feature_enc_layers]) | |
| self.feat2tar_ratio = cfg.label_rate * feature_ds_rate / 16000 | |
| self.post_extract_proj = (nn.Linear(self.embed, cfg.encoder_embed_dim) if self.embed != cfg.encoder_embed_dim else None) | |
| self.mask_prob = cfg.mask_prob | |
| self.mask_selection = cfg.mask_selection | |
| self.mask_other = cfg.mask_other | |
| self.mask_length = cfg.mask_length | |
| self.no_mask_overlap = cfg.no_mask_overlap | |
| self.mask_min_space = cfg.mask_min_space | |
| self.mask_channel_prob = cfg.mask_channel_prob | |
| self.mask_channel_selection = cfg.mask_channel_selection | |
| self.mask_channel_other = cfg.mask_channel_other | |
| self.mask_channel_length = cfg.mask_channel_length | |
| self.no_mask_channel_overlap = cfg.no_mask_channel_overlap | |
| self.mask_channel_min_space = cfg.mask_channel_min_space | |
| self.dropout_input = nn.Dropout(cfg.dropout_input) | |
| self.dropout_features = nn.Dropout(cfg.dropout_features) | |
| self.feature_grad_mult = cfg.feature_grad_mult | |
| self.logit_temp = cfg.logit_temp | |
| self.skip_masked = cfg.skip_masked | |
| self.skip_nomask = cfg.skip_nomask | |
| final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim | |
| self.mask_emb = nn.Parameter(torch.FloatTensor(cfg.encoder_embed_dim).uniform_()) | |
| self.encoder = TransformerEncoder(cfg) | |
| self.layer_norm = LayerNorm(self.embed) | |
| self.target_glu = None | |
| if cfg.target_glu: self.target_glu = nn.Sequential(nn.Linear(final_dim, final_dim * 2), nn.GLU()) | |
| self.untie_final_proj = cfg.untie_final_proj | |
| self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim) | |
| self.num_classes = [504] | |
| self.label_embs_concat = nn.Parameter(torch.FloatTensor(sum(self.num_classes), final_dim)) | |
| nn.init.uniform_(self.label_embs_concat) | |
| def upgrade_state_dict_named(self, state_dict, name): | |
| super().upgrade_state_dict_named(state_dict, name) | |
| return state_dict | |
| def apply_mask(self, x, padding_mask, target_list): | |
| B, T, C = x.shape | |
| if self.mask_prob > 0: | |
| mask_indices = torch.from_numpy(compute_mask_indices((B, T), padding_mask, self.mask_prob, self.mask_length, self.mask_selection, self.mask_other, min_masks=2, no_overlap=self.no_mask_overlap, min_space=self.mask_min_space)).to(x.device) | |
| x[mask_indices] = self.mask_emb | |
| else: mask_indices = None | |
| if self.mask_channel_prob > 0: x[(torch.from_numpy(compute_mask_indices((B, C), None, self.mask_channel_prob, self.mask_channel_length, self.mask_channel_selection, self.mask_channel_other, no_overlap=self.no_mask_channel_overlap, min_space=self.mask_channel_min_space)).to(x.device).unsqueeze(1).expand(-1, T, -1))] = 0 | |
| return x, mask_indices | |
| def compute_nce(self, x, pos, negs): | |
| neg_is_pos = (pos == negs).all(-1) | |
| logits = torch.cosine_similarity(x.float(), torch.cat([pos.unsqueeze(0), negs], dim=0).float(), dim=-1).type_as(x) | |
| logits /= self.logit_temp | |
| if neg_is_pos.any(): logits[1:][neg_is_pos] = float("-inf") | |
| return logits.transpose(0, 1) | |
| def forward_features(self, source): | |
| if self.feature_grad_mult > 0: | |
| features = self.feature_extractor(source) | |
| if self.feature_grad_mult != 1.0: features = GradMultiply.apply(features, self.feature_grad_mult) | |
| else: | |
| with torch.no_grad(): | |
| features = self.feature_extractor(source) | |
| return features | |
| def forward_targets(self, features, target_list): | |
| feat_tsz = features.size(2) | |
| targ_tsz = min([t.size(1) for t in target_list]) | |
| if self.feat2tar_ratio * feat_tsz > targ_tsz: | |
| feat_tsz = int(targ_tsz / self.feat2tar_ratio) | |
| features = features[..., :feat_tsz] | |
| return features, [t[:, (torch.arange(feat_tsz).float() * self.feat2tar_ratio).long()] for t in target_list] | |
| def forward_padding_mask(self, features, padding_mask): | |
| extra = padding_mask.size(1) % features.size(1) | |
| if extra > 0: padding_mask = padding_mask[:, :-extra] | |
| return padding_mask.view(padding_mask.size(0), features.size(1), -1).all(-1) | |
| def forward(self, source, target_list = None, padding_mask = None, mask = True, features_only = False, output_layer = None): | |
| features = self.forward_features(source) | |
| if target_list is not None: features, target_list = self.forward_targets(features, target_list) | |
| features_pen = features.float().pow(2).mean() | |
| features = self.layer_norm(features.transpose(1, 2)) | |
| unmasked_features = features.clone() | |
| if padding_mask is not None: padding_mask = self.forward_padding_mask(features, padding_mask) | |
| if self.post_extract_proj is not None: features = self.post_extract_proj(features) | |
| features = self.dropout_input(features) | |
| unmasked_features = self.dropout_features(unmasked_features) | |
| if mask: x, mask_indices = self.apply_mask(features, padding_mask, target_list) | |
| else: x, mask_indices = features, None | |
| x, _ = self.encoder(x, padding_mask=padding_mask, layer=None if output_layer is None else output_layer - 1) | |
| if features_only: return {"x": x, "padding_mask": padding_mask, "features": features} | |
| def compute_pred(proj_x, target, label_embs): | |
| y = torch.index_select(label_embs, 0, target.long()) | |
| negs = label_embs.unsqueeze(1).expand(-1, proj_x.size(0), -1) | |
| if self.target_glu: | |
| y = self.target_glu(y) | |
| negs = self.target_glu(negs) | |
| return self.compute_nce(proj_x, y, negs) | |
| label_embs_list = self.label_embs_concat.split(self.num_classes, 0) | |
| if not self.skip_masked: | |
| masked_indices = torch.logical_and(~padding_mask, mask_indices) | |
| proj_x_m = self.final_proj(x[masked_indices]) | |
| logit_m_list = [compute_pred(proj_x_m, t[masked_indices], label_embs_list[i]) for i, (proj_x_m, t) in enumerate(zip(proj_x_m.chunk(len(target_list), dim=-1) if self.untie_final_proj else [proj_x_m for _ in range(len(target_list))], target_list))] | |
| else: logit_m_list = [None for _ in target_list] | |
| if not self.skip_nomask: | |
| nomask_indices = torch.logical_and(~padding_mask, ~mask_indices) | |
| proj_x_u = self.final_proj(x[nomask_indices]) | |
| logit_u_list = [compute_pred(proj_x_u, t[nomask_indices], label_embs_list[i]) for i, (proj_x_u, t) in enumerate(zip(proj_x_u.chunk(len(target_list), dim=-1) if self.untie_final_proj else [proj_x_u for _ in range(len(target_list))], target_list))] | |
| else: logit_u_list = [None for _ in target_list] | |
| return {"logit_m_list": logit_m_list, "logit_u_list": logit_u_list, "padding_mask": padding_mask, "features_pen": features_pen} | |
| def extract_features(self, source, padding_mask = None, mask = False, ret_conv = False, output_layer = None): | |
| res = self.forward(source, padding_mask=padding_mask, mask=mask, features_only=True, output_layer=output_layer) | |
| return res["features"] if ret_conv else res["x"], res["padding_mask"] | |
| def get_logits(self, net_output, is_masked=True): | |
| return [x.float() for x in (net_output["logit_m_list"] if is_masked else net_output["logit_u_list"]) if x is not None] | |
| def get_targets(self, net_output, is_masked=True): | |
| return [x.new_zeros(x.size(0), dtype=torch.long) for x in self.get_logits(net_output, is_masked)] | |
| def get_extra_losses(self, net_output): | |
| extra_losses, names = [], [] | |
| if "features_pen" in net_output: | |
| extra_losses.append(net_output["features_pen"]) | |
| names.append("features_pen") | |
| return extra_losses, names | |
| def remove_pretraining_modules(self): | |
| self.target_glu = None | |
| self.final_proj = None |