From 8f596af4be1c2e5c4e4b4a7008ba96f412d40fca Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 29 四月 2024 14:32:43 +0800
Subject: [PATCH] batch
---
funasr/models/sense_voice/decoder.py | 536 +++++++++++++++++++++++++++++++----------------------------
1 files changed, 284 insertions(+), 252 deletions(-)
diff --git a/funasr/models/sense_voice/decoder.py b/funasr/models/sense_voice/decoder.py
index 9fdb3bd..133508f 100644
--- a/funasr/models/sense_voice/decoder.py
+++ b/funasr/models/sense_voice/decoder.py
@@ -31,275 +31,307 @@
)
-
def sense_voice_decode_forward(
- self,
- x: torch.Tensor,
- xa: torch.Tensor,
- kv_cache: Optional[dict] = None,
- **kwargs,
+ self,
+ x: torch.Tensor,
+ xa: torch.Tensor,
+ kv_cache: Optional[dict] = None,
+ **kwargs,
):
- """Forward decoder.
+ """Forward decoder.
- Args:
- hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
- hlens: (batch)
- ys_in_pad:
- input token ids, int64 (batch, maxlen_out)
- if input_layer == "embed"
- input tensor (batch, maxlen_out, #mels) in the other cases
- ys_in_lens: (batch)
- Returns:
- (tuple): tuple containing:
+ Args:
+ hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
+ hlens: (batch)
+ ys_in_pad:
+ input token ids, int64 (batch, maxlen_out)
+ if input_layer == "embed"
+ input tensor (batch, maxlen_out, #mels) in the other cases
+ ys_in_lens: (batch)
+ Returns:
+ (tuple): tuple containing:
- x: decoded token score before softmax (batch, maxlen_out, token)
- if use_output_layer is True,
- olens: (batch, )
- """
- # import pdb;pdb.set_trace()
- use_padmask = self.use_padmask
- hlens = kwargs.get("hlens", None)
-
- ys_in_lens = kwargs.get("ys_in_lens", None)
-
- offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
- tgt, memory = x, xa
- tgt[tgt == -1] = 0
- tgt = (
- self.token_embedding(tgt)
- + self.positional_embedding[offset: offset + tgt.size(1)]
- )
- # tgt = self.dropout(tgt)
-
- x = tgt.to(memory.dtype)
-
- if use_padmask and hlens is not None:
- memory_mask = (~make_pad_mask(hlens)[:, None, :]).to(memory.device)
- else:
- memory_mask = None
-
- for layer, block in enumerate(self.blocks):
- x = block(x, memory, mask=self.mask, memory_mask=memory_mask, is_pad_mask=False, is_pad_memory_mask=True)
-
- x = self.ln(x)
- x = (
- x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)
- ).float()
-
- return x
+ x: decoded token score before softmax (batch, maxlen_out, token)
+ if use_output_layer is True,
+ olens: (batch, )
+ """
+ # import pdb;pdb.set_trace()
+ use_padmask = self.use_padmask
+ hlens = kwargs.get("hlens", None)
+
+ ys_in_lens = kwargs.get("ys_in_lens", None)
+
+ offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
+ tgt, memory = x, xa
+ tgt[tgt == -1] = 0
+ tgt = self.token_embedding(tgt) + self.positional_embedding[offset : offset + tgt.size(1)]
+ # tgt = self.dropout(tgt)
+
+ x = tgt.to(memory.dtype)
+
+ if use_padmask and hlens is not None:
+ memory_mask = (~make_pad_mask(hlens)[:, None, :]).to(memory.device)
+ else:
+ memory_mask = None
+
+ for layer, block in enumerate(self.blocks):
+ x = block(
+ x,
+ memory,
+ mask=self.mask,
+ memory_mask=memory_mask,
+ is_pad_mask=False,
+ is_pad_memory_mask=True,
+ )
+
+ x = self.ln(x)
+ x = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
+
+ return x
class MultiHeadAttention(nn.Module):
- def __init__(self, n_state: int, n_head: int):
- super().__init__()
- self.n_head = n_head
- self.query = Linear(n_state, n_state)
- self.key = Linear(n_state, n_state, bias=False)
- self.value = Linear(n_state, n_state)
- self.out = Linear(n_state, n_state)
-
- def forward(
- self,
- x: Tensor,
- xa: Optional[Tensor] = None,
- mask: Optional[Tensor] = None,
- kv_cache: Optional[dict] = None,
- **kwargs,
- ):
- is_pad_mask = kwargs.get("is_pad_mask", False)
-
- q = self.query(x)
-
- if kv_cache is None or xa is None or self.key not in kv_cache:
- # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
- # otherwise, perform key/value projections for self- or cross-attention as usual.
- k = self.key(x if xa is None else xa)
- v = self.value(x if xa is None else xa)
- else:
- # for cross-attention, calculate keys and values once and reuse in subsequent calls.
- k = kv_cache[self.key]
- v = kv_cache[self.value]
-
- wv, qk = self.qkv_attention(q, k, v, mask, is_pad_mask=is_pad_mask)
- return self.out(wv), qk
-
- def qkv_attention(
- self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None, **kwargs,
- ):
- is_pad_mask = kwargs.get("is_pad_mask", False)
- n_batch, n_ctx, n_state = q.shape
- scale = (n_state // self.n_head) ** -0.25
- q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
- k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
- v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
-
- qk = q @ k
- if mask is not None:
- if not is_pad_mask:
- qk = qk + mask[:n_ctx, :n_ctx]
- else:
- mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
- min_value = float(
- np.finfo(torch.tensor(0, dtype=qk.dtype).numpy().dtype).min
- )
- qk = qk.masked_fill(mask, min_value)
-
- qk = qk.float()
-
- w = F.softmax(qk, dim=-1).to(q.dtype)
- if mask is not None and is_pad_mask:
- w = w.masked_fill(mask, 0.0)
- return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
+ def __init__(self, n_state: int, n_head: int):
+ super().__init__()
+ self.n_head = n_head
+ self.query = Linear(n_state, n_state)
+ self.key = Linear(n_state, n_state, bias=False)
+ self.value = Linear(n_state, n_state)
+ self.out = Linear(n_state, n_state)
+
+ def forward(
+ self,
+ x: Tensor,
+ xa: Optional[Tensor] = None,
+ mask: Optional[Tensor] = None,
+ kv_cache: Optional[dict] = None,
+ **kwargs,
+ ):
+ is_pad_mask = kwargs.get("is_pad_mask", False)
+
+ q = self.query(x)
+
+ if kv_cache is None or xa is None or self.key not in kv_cache:
+ # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
+ # otherwise, perform key/value projections for self- or cross-attention as usual.
+ k = self.key(x if xa is None else xa)
+ v = self.value(x if xa is None else xa)
+ else:
+ # for cross-attention, calculate keys and values once and reuse in subsequent calls.
+ k = kv_cache[self.key]
+ v = kv_cache[self.value]
+
+ wv, qk = self.qkv_attention(q, k, v, mask, is_pad_mask=is_pad_mask)
+ return self.out(wv), qk
+
+ def qkv_attention(
+ self,
+ q: Tensor,
+ k: Tensor,
+ v: Tensor,
+ mask: Optional[Tensor] = None,
+ **kwargs,
+ ):
+ is_pad_mask = kwargs.get("is_pad_mask", False)
+ n_batch, n_ctx, n_state = q.shape
+ scale = (n_state // self.n_head) ** -0.25
+ q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
+ k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
+ v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
+
+ qk = q @ k
+ if mask is not None:
+ if not is_pad_mask:
+ qk = qk + mask[:n_ctx, :n_ctx]
+ else:
+ mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
+ min_value = float(np.finfo(torch.tensor(0, dtype=qk.dtype).numpy().dtype).min)
+ qk = qk.masked_fill(mask, min_value)
+
+ qk = qk.float()
+
+ w = F.softmax(qk, dim=-1).to(q.dtype)
+ if mask is not None and is_pad_mask:
+ w = w.masked_fill(mask, 0.0)
+ return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
-from funasr.models.sense_voice.rwkv_v6 import RWKVLayer
from omegaconf import OmegaConf
class ResidualAttentionBlockRWKV(nn.Module):
- def __init__(self, n_state: int, n_head: int, cross_attention: bool = False, layer_id=0, **kwargs):
- super().__init__()
-
- rwkv_cfg = kwargs.get("rwkv_cfg", {})
- args = OmegaConf.create(rwkv_cfg)
- self.attn = RWKVLayer(args=args, layer_id=layer_id)
- if args.get("datatype", "bf16") == "bf16":
- self.attn.to(torch.bfloat16)
-
- self.ln0 = None
- if layer_id == 0 and not args.get("ln0", True):
- self.ln0 = LayerNorm(args.n_embd)
- if args.get("init_rwkv", True):
- print("init_rwkv")
- layer_id = 0
- scale = ((1 + layer_id) / args.get("n_layer")) ** 0.7
- nn.init.constant_(self.ln0.weight, scale)
- self.layer_id = layer_id
- self.args = args
-
- self.ln1 = None
- if not args.get("ln1", True):
- self.ln1 = LayerNorm(args.n_embd)
- # init
- if args.get("init_rwkv", True):
- print("init_rwkv")
- scale = ((1 + layer_id) / args.get("n_layer")) ** 0.7
- nn.init.constant_(self.ln1.weight, scale)
-
- self.cross_attn = (
- MultiHeadAttention(n_state, n_head) if cross_attention else None
- )
- self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
-
- n_mlp = n_state * 4
- self.mlp = nn.Sequential(
- Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)
- )
- self.mlp_ln = LayerNorm(n_state)
-
- def forward(
- self,
- x: Tensor,
- xa: Optional[Tensor] = None,
- mask: Optional[Tensor] = None,
- kv_cache: Optional[dict] = None,
- **kwargs,
- ):
- is_pad_mask = kwargs.get("is_pad_mask", False)
- is_pad_memory_mask = kwargs.get("is_pad_memory_mask", False)
-
- if self.layer_id == 0 and self.ln0 is not None:
- x = self.ln0(x)
-
- if self.ln1 is None:
- x = x + self.attn(x, mask=mask, kv_cache=kv_cache, is_pad_mask=is_pad_mask)[0]
- else:
- x = x + self.attn(self.ln1(x), mask=mask, kv_cache=kv_cache, is_pad_mask=is_pad_mask)[0]
-
- if self.cross_attn:
- x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache, is_pad_mask=is_pad_memory_mask)[0]
- x = x + self.mlp(self.mlp_ln(x))
-
- return x
+ def __init__(
+ self, n_state: int, n_head: int, cross_attention: bool = False, layer_id=0, **kwargs
+ ):
+ super().__init__()
+
+ rwkv_cfg = kwargs.get("rwkv_cfg", {})
+ args = OmegaConf.create(rwkv_cfg)
+ if args.get("version", "v4") == "v4":
+ from funasr.models.sense_voice.rwkv_v4 import RWKVLayer
+ from funasr.models.sense_voice.rwkv_v4 import RWKV_TimeMix as RWKV_Tmix
+ elif args.get("version", "v5") == "v5":
+ from funasr.models.sense_voice.rwkv_v5 import RWKVLayer
+ from funasr.models.sense_voice.rwkv_v5 import RWKV_Tmix_x052 as RWKV_Tmix
+ else:
+ from funasr.models.sense_voice.rwkv_v6 import RWKVLayer
+ from funasr.models.sense_voice.rwkv_v6 import RWKV_Tmix_x060 as RWKV_Tmix
+ # self.att = RWKVLayer(args=args, layer_id=layer_id)
+ self.att = RWKV_Tmix(args, layer_id=layer_id)
+
+ if args.get("init_rwkv", True):
+ print("init_rwkv")
+ nn.init.orthogonal_(self.att.receptance.weight, gain=1)
+ nn.init.orthogonal_(self.att.key.weight, gain=0.1)
+ nn.init.orthogonal_(self.att.value.weight, gain=1)
+ nn.init.orthogonal_(self.att.gate.weight, gain=0.1)
+ nn.init.zeros_(self.att.output.weight)
+
+ self.ln0 = None
+ if layer_id == 0 and not args.get("ln0", True):
+ self.ln0 = LayerNorm(args.n_embd)
+ if args.get("init_rwkv", True):
+ print("init_rwkv")
+ layer_id = 0
+ scale = ((1 + layer_id) / args.get("n_layer")) ** 0.7
+ nn.init.constant_(self.ln0.weight, scale)
+
+ self.layer_id = layer_id
+ self.args = args
+
+ self.ln1 = None
+ if not args.get("ln1", True):
+ self.ln1 = LayerNorm(args.n_embd)
+ # init
+ if args.get("init_rwkv", True):
+ print("init_rwkv")
+ scale = ((1 + layer_id) / args.get("n_layer")) ** 0.7
+ nn.init.constant_(self.ln1.weight, scale)
+
+ if args.get("datatype", "bf16") == "bf16":
+ self.att.to(torch.bfloat16)
+ # if self.ln1 is not None:
+ # self.ln1.to(torch.bfloat16)
+
+ self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None
+ self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
+
+ n_mlp = n_state * 4
+ self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
+ self.mlp_ln = LayerNorm(n_state)
+
+ def forward(
+ self,
+ x: Tensor,
+ xa: Optional[Tensor] = None,
+ mask: Optional[Tensor] = None,
+ kv_cache: Optional[dict] = None,
+ **kwargs,
+ ):
+ is_pad_mask = kwargs.get("is_pad_mask", False)
+ is_pad_memory_mask = kwargs.get("is_pad_memory_mask", False)
+
+ if self.layer_id == 0 and self.ln0 is not None:
+ x = self.ln0(x)
+
+ if self.args.get("datatype", "bf16") == "bf16":
+ x = x.bfloat16()
+ if self.ln1 is None:
+ x = x + self.att(x, mask=mask, kv_cache=kv_cache, is_pad_mask=is_pad_mask)[0]
+ else:
+ x = x + self.att(self.ln1(x), mask=mask, kv_cache=kv_cache, is_pad_mask=is_pad_mask)[0]
+ if self.args.get("datatype", "bf16") == "bf16":
+ x = x.to(torch.float32)
+
+ if self.cross_attn:
+ x = (
+ x
+ + self.cross_attn(
+ self.cross_attn_ln(x), xa, kv_cache=kv_cache, is_pad_mask=is_pad_memory_mask
+ )[0]
+ )
+ x = x + self.mlp(self.mlp_ln(x))
+
+ return x
+
@tables.register("decoder_classes", "SenseVoiceDecoder")
class SenseVoiceDecoder(nn.Module):
- def __init__(
- self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int, **kwargs
- ):
- super().__init__()
-
- self.token_embedding = nn.Embedding(n_vocab, n_state)
- self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
+ def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int, **kwargs):
+ super().__init__()
+ self.token_embedding = nn.Embedding(n_vocab, n_state)
+ self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
- self.blocks = nn.ModuleList(
- [
- ResidualAttentionBlockRWKV(n_state, n_head, cross_attention=True, layer_id=i, **kwargs)
- for i in range(n_layer)
- ]
- )
- self.ln = LayerNorm(n_state)
-
- mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
- self.register_buffer("mask", mask, persistent=False)
-
- self.use_padmask = kwargs.get("use_padmask", True)
+ self.blocks = nn.ModuleList(
+ [
+ ResidualAttentionBlockRWKV(
+ n_state, n_head, cross_attention=True, layer_id=i, **kwargs
+ )
+ for i in range(n_layer)
+ ]
+ )
+ self.ln = LayerNorm(n_state)
+ mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
+ self.register_buffer("mask", mask, persistent=False)
-
- def forward(
- self,
- x: torch.Tensor,
- xa: torch.Tensor,
- kv_cache: Optional[dict] = None,
- **kwargs,
- ):
- """Forward decoder.
-
- Args:
- hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
- hlens: (batch)
- ys_in_pad:
- input token ids, int64 (batch, maxlen_out)
- if input_layer == "embed"
- input tensor (batch, maxlen_out, #mels) in the other cases
- ys_in_lens: (batch)
- Returns:
- (tuple): tuple containing:
-
- x: decoded token score before softmax (batch, maxlen_out, token)
- if use_output_layer is True,
- olens: (batch, )
- """
- # import pdb;pdb.set_trace()
- use_padmask = self.use_padmask
- hlens = kwargs.get("hlens", None)
-
- ys_in_lens = kwargs.get("ys_in_lens", None)
-
- offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
- tgt, memory = x, xa
- tgt[tgt == -1] = 0
- tgt = (
- self.token_embedding(tgt)
- + self.positional_embedding[offset: offset + tgt.size(1)]
- )
- # tgt = self.dropout(tgt)
-
- x = tgt.to(memory.dtype)
-
- if use_padmask and hlens is not None:
- memory_mask = (~make_pad_mask(hlens)[:, None, :]).to(memory.device)
- else:
- memory_mask = None
-
- for layer, block in enumerate(self.blocks):
- x = block(x, memory, mask=self.mask, memory_mask=memory_mask, is_pad_mask=False, is_pad_memory_mask=True)
-
- x = self.ln(x)
- x = (
- x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)
- ).float()
-
- return x
+ self.use_padmask = kwargs.get("use_padmask", True)
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ xa: torch.Tensor,
+ kv_cache: Optional[dict] = None,
+ **kwargs,
+ ):
+ """Forward decoder.
+
+ Args:
+ hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
+ hlens: (batch)
+ ys_in_pad:
+ input token ids, int64 (batch, maxlen_out)
+ if input_layer == "embed"
+ input tensor (batch, maxlen_out, #mels) in the other cases
+ ys_in_lens: (batch)
+ Returns:
+ (tuple): tuple containing:
+
+ x: decoded token score before softmax (batch, maxlen_out, token)
+ if use_output_layer is True,
+ olens: (batch, )
+ """
+ # import pdb;pdb.set_trace()
+ use_padmask = self.use_padmask
+ hlens = kwargs.get("hlens", None)
+
+ ys_in_lens = kwargs.get("ys_in_lens", None)
+
+ offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
+ tgt, memory = x, xa
+ tgt[tgt == -1] = 0
+ tgt = self.token_embedding(tgt) + self.positional_embedding[offset : offset + tgt.size(1)]
+ # tgt = self.dropout(tgt)
+
+ x = tgt.to(memory.dtype)
+
+ if use_padmask and hlens is not None:
+ memory_mask = (~make_pad_mask(hlens)[:, None, :]).to(memory.device)
+ else:
+ memory_mask = None
+
+ for layer, block in enumerate(self.blocks):
+ x = block(
+ x,
+ memory,
+ mask=self.mask,
+ memory_mask=memory_mask,
+ is_pad_mask=False,
+ is_pad_memory_mask=True,
+ )
+
+ x = self.ln(x)
+ x = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
+
+ return x
--
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