mirrors/icefall
1
0
Fork 0
mirror of https://github.com/k2-fsa/icefall.git synced 2025-08-27 02:34:21 +00:00
Code Issues Packages Projects Releases Wiki Activity

fix for black

Browse Source
This commit is contained in:
yifanyeung 2024-02-18 12:44:44 +08:00
parent b070d04ae8
commit 809bdb07f0
9 changed files with 179 additions and 56 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

161
egs/librispeech/SSL/hubert/attention_module.py
View File

@ -155,7 +155,8 @@ class MultiheadAttention(nn.Module):
self.encoder_decoder_attention = encoder_decoder_attention
assert not self.self_attention or self.qkv_same_dim, (
"Self-attention requires query, key and " "value to be of the same size"
"Self-attention requires query, key and "
"value to be of the same size"
)
self.k_proj = quant_noise(
@ -217,22 +218,36 @@ class MultiheadAttention(nn.Module):
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()
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()
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()
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]
k_proj_heads_norm[i]
+ q_proj_heads_norm[i]
+ v_proj_heads_norm[i]
)
sorted_head_index = sorted(
@ -266,7 +281,9 @@ class MultiheadAttention(nn.Module):
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_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()
@ -313,7 +330,9 @@ class MultiheadAttention(nn.Module):
) -> Tuple[Optional[Tensor], Optional[Tensor]]:
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)
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(
@ -351,10 +370,12 @@ class MultiheadAttention(nn.Module):
) -> Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]:
zero_attn_shape = k.size()[:-2] + torch.Size([1]) + k.size()[-1:]
k = torch.cat(
[k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=-2
[k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)],
dim=-2,
)
v = torch.cat(
[v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=-2
[v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)],
dim=-2,
)
key_padding_mask, attn_mask = self._pad_masks(
key_padding_mask=key_padding_mask, attn_mask=attn_mask
@ -367,7 +388,9 @@ class MultiheadAttention(nn.Module):
key: Optional[Tensor],
value: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
incremental_state: Optional[
Dict[str, Dict[str, Optional[Tensor]]]
] = None,
need_weights: bool = True,
static_kv: bool = False,
attn_mask: Optional[Tensor] = None,
@ -432,7 +455,9 @@ class MultiheadAttention(nn.Module):
self.embed_dim,
self.num_heads,
torch.empty([0]),
torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
torch.cat(
(self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)
),
self.bias_k,
self.bias_v,
self.add_zero_attn,
@ -440,7 +465,9 @@ class MultiheadAttention(nn.Module):
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,
key_padding_mask.bool()
if key_padding_mask is not None
else None,
need_weights,
attn_mask,
use_separate_proj_weight=True,
@ -455,7 +482,10 @@ class MultiheadAttention(nn.Module):
# previous time steps are cached - no need to recompute
# key and value if they are static
if static_kv:
assert self.encoder_decoder_attention and not self.self_attention
assert (
self.encoder_decoder_attention
and not self.self_attention
)
key = value = None
else:
saved_state = None
@ -473,9 +503,9 @@ class MultiheadAttention(nn.Module):
else:
if self.beam_size > 1 and bsz == key.size(1):
# key is [T, bsz*beam_size, C], reduce to [T, bsz, C]
key = key.view(key.size(0), -1, self.beam_size, key.size(2))[
:, :, 0, :
]
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)
@ -522,7 +552,9 @@ class MultiheadAttention(nn.Module):
_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)
prev_key = _prev_key.view(
kv_bsz * self.num_heads, -1, self.head_dim
)
if static_kv:
k = prev_key
else:
@ -553,14 +585,18 @@ class MultiheadAttention(nn.Module):
static_kv=static_kv,
)
saved_state["prev_key"] = k.view(kv_bsz, self.num_heads, -1, self.head_dim)
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
# In this branch incremental_state is never None
assert incremental_state is not None
incremental_state = self._set_input_buffer(incremental_state, saved_state)
incremental_state = self._set_input_buffer(
incremental_state, saved_state
)
assert k is not None
assert k.size(1) == src_len
@ -586,12 +622,20 @@ class MultiheadAttention(nn.Module):
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:])
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)
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]
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)
@ -601,7 +645,9 @@ class MultiheadAttention(nn.Module):
if key_padding_mask is not None:
# don't attend to padding symbols
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(
bsz, self.num_heads, tgt_len, src_len
)
if not is_tpu:
attn_weights = attn_weights.view(
kv_bsz, -1, self.num_heads, tgt_len, src_len
@ -615,9 +661,13 @@ class MultiheadAttention(nn.Module):
)
else:
attn_weights = attn_weights.transpose(0, 2)
attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf"))
attn_weights = attn_weights.masked_fill(
key_padding_mask, float("-inf")
)
attn_weights = attn_weights.transpose(0, 2)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(
bsz * self.num_heads, tgt_len, src_len
)
if before_softmax:
return attn_weights, v
@ -652,13 +702,21 @@ class MultiheadAttention(nn.Module):
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]
assert list(attn.size()) == [
bsz * self.num_heads,
tgt_len,
self.head_dim,
]
if self.onnx_trace and attn.size(1) == 1:
# when ONNX tracing a single decoder step (sequence length == 1)
# the transpose is a no-op copy before view, thus unnecessary
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 = (
attn.transpose(0, 1)
.contiguous()
.view(tgt_len, bsz, self.embed_dim)
)
attn = self.out_proj(attn)
attn_weights: Optional[Tensor] = None
if need_weights:
@ -728,7 +786,9 @@ class MultiheadAttention(nn.Module):
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):
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(
@ -737,10 +797,16 @@ class MultiheadAttention(nn.Module):
// self.beam_size,
)
else:
input_buffer[k] = input_buffer_k.index_select(0, new_order)
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)
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):
@ -748,7 +814,8 @@ class MultiheadAttention(nn.Module):
self.beam_size = beam_size
def _get_input_buffer(
self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
self,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
) -> Dict[str, Optional[Tensor]]:
result = self.get_incremental_state(incremental_state, "attn_state")
if result is not None:
@ -762,9 +829,13 @@ class MultiheadAttention(nn.Module):
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
buffer: Dict[str, Optional[Tensor]],
):
return self.set_incremental_state(incremental_state, "attn_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):
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):
@ -776,19 +847,27 @@ class MultiheadAttention(nn.Module):
# in_proj_weight used to be q + k + v with same dimensions
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 :]
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 + "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 :]
items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][
2 * dim :
]
keys_to_remove.append(prefix + "in_proj_bias")

4
egs/librispeech/SSL/hubert/finetune.py
View File

@ -925,7 +925,9 @@ def train_one_epoch(
tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
if params.use_fp16:
tb_writer.add_scalar(
"train/grad_scale", cur_grad_scale, params.batch_idx_train
"train/grad_scale",
cur_grad_scale,
params.batch_idx_train,
)
if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:

4
egs/librispeech/SSL/hubert/finetune_ce.py
View File

@ -925,7 +925,9 @@ def train_one_epoch(
tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
if params.use_fp16:
tb_writer.add_scalar(
"train/grad_scale", cur_grad_scale, params.batch_idx_train
"train/grad_scale",
cur_grad_scale,
params.batch_idx_train,
)
if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:

24
egs/librispeech/SSL/hubert/hubert.py
View File

@ -251,7 +251,10 @@ def add_hubert_arguments(parser: argparse.ArgumentParser):
)
parser.add_argument(
"--encoder-embed-dim", type=int, default=768, help="encoder embedding dimension"
"--encoder-embed-dim",
type=int,
default=768,
help="encoder embedding dimension",
)
parser.add_argument(
@ -271,7 +274,14 @@ def add_hubert_arguments(parser: argparse.ArgumentParser):
parser.add_argument(
"--activation-fn",
type=str,
choices=["relu", "gelu", "gelu_fast", "gelu_accurate", "tanh", "linear"],
choices=[
"relu",
"gelu",
"gelu_fast",
"gelu_accurate",
"tanh",
"linear",
],
default="gelu",
help="activation function to use",
)
@ -356,11 +366,17 @@ def add_hubert_arguments(parser: argparse.ArgumentParser):
)
parser.add_argument(
"--conv-bias", type=bool, default=False, help="include bias in conv encoder"
"--conv-bias",
type=bool,
default=False,
help="include bias in conv encoder",
)
parser.add_argument(
"--logit-temp", type=float, default=0.1, help="temperature to divide logits by"
"--logit-temp",
type=float,
default=0.1,
help="temperature to divide logits by",
)
parser.add_argument(

24
egs/librispeech/SSL/hubert/hubert_ce.py
View File

@ -251,7 +251,10 @@ def add_hubert_arguments(parser: argparse.ArgumentParser):
)
parser.add_argument(
"--encoder-embed-dim", type=int, default=768, help="encoder embedding dimension"
"--encoder-embed-dim",
type=int,
default=768,
help="encoder embedding dimension",
)
parser.add_argument(
@ -271,7 +274,14 @@ def add_hubert_arguments(parser: argparse.ArgumentParser):
parser.add_argument(
"--activation-fn",
type=str,
choices=["relu", "gelu", "gelu_fast", "gelu_accurate", "tanh", "linear"],
choices=[
"relu",
"gelu",
"gelu_fast",
"gelu_accurate",
"tanh",
"linear",
],
default="gelu",
help="activation function to use",
)
@ -356,11 +366,17 @@ def add_hubert_arguments(parser: argparse.ArgumentParser):
)
parser.add_argument(
"--conv-bias", type=bool, default=False, help="include bias in conv encoder"
"--conv-bias",
type=bool,
default=False,
help="include bias in conv encoder",
)
parser.add_argument(
"--logit-temp", type=float, default=0.1, help="temperature to divide logits by"
"--logit-temp",
type=float,
default=0.1,
help="temperature to divide logits by",
)
parser.add_argument(

4
egs/librispeech/SSL/hubert/pretrain.py
View File

@ -744,7 +744,9 @@ def train_one_epoch(
tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
if params.use_fp16:
tb_writer.add_scalar(
"train/grad_scale", cur_grad_scale, params.batch_idx_train
"train/grad_scale",
cur_grad_scale,
params.batch_idx_train,
)
if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:

4
egs/librispeech/SSL/hubert/pretrain_ce.py
View File

@ -744,7 +744,9 @@ def train_one_epoch(
tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
if params.use_fp16:
tb_writer.add_scalar(
"train/grad_scale", cur_grad_scale, params.batch_idx_train
"train/grad_scale",
cur_grad_scale,
params.batch_idx_train,
)
if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:

3
egs/librispeech/SSL/hubert/utils.py
View File

@ -254,7 +254,8 @@ def quant_noise(module, p, block_size):
# split weight matrix into blocks and randomly drop selected blocks
mask = torch.zeros(
in_features // block_size * out_features, device=weight.device
in_features // block_size * out_features,
device=weight.device,
)
mask.bernoulli_(p)
mask = mask.repeat_interleave(block_size, -1).view(-1, in_features)

7
egs/librispeech/SSL/hubert/wav2vec2_module.py
View File

@ -309,11 +309,14 @@ class TransformerEncoder(nn.Module):
# layer_check = layer.unwrapped_module
if (corpus_key is None) or (
not isinstance(
layer_check, (TransformerSentenceEncoderWithAdapterLayer,)
layer_check,
(TransformerSentenceEncoderWithAdapterLayer,),
)
):
x, (z, lr) = layer(
x, self_attn_padding_mask=padding_mask, need_weights=False
x,
self_attn_padding_mask=padding_mask,
need_weights=False,
)
else:
x, (z, lr) = layer(