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use average value as memory vector for each chunk
This commit is contained in:
yaozengwei
parent
1c067e7364
commit
193b44ed7a
@ -537,7 +537,6 @@ class EmformerAttention(nn.Module):
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self,
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utterance: torch.Tensor,
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right_context: torch.Tensor,
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summary: torch.Tensor,
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memory: torch.Tensor,
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attention_mask: torch.Tensor,
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padding_mask: Optional[torch.Tensor] = None,
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@ -550,10 +549,8 @@ class EmformerAttention(nn.Module):
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M = memory.size(0)
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scaling = float(self.head_dim) ** -0.5
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# compute query with [right_context, utterance, summary].
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query = self.emb_to_query(
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torch.cat([right_context, utterance, summary])
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)
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# compute query with [right_context, utterance].
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query = self.emb_to_query(torch.cat([right_context, utterance]))
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# compute key and value with [memory, right_context, utterance].
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key, value = self.emb_to_key_value(
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torch.cat([memory, right_context, utterance])
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@ -593,26 +590,18 @@ class EmformerAttention(nn.Module):
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)
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# apply output projection
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outputs = self.out_proj(attention)
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output_right_context_utterance = self.out_proj(attention)
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output_right_context_utterance = outputs[: R + U]
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output_memory = outputs[R + U :]
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if self.tanh_on_mem:
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output_memory = torch.tanh(output_memory)
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else:
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output_memory = torch.clamp(output_memory, min=-10, max=10)
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return output_right_context_utterance, output_memory, key, value
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return output_right_context_utterance, key, value
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def forward(
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self,
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utterance: torch.Tensor,
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right_context: torch.Tensor,
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summary: torch.Tensor,
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memory: torch.Tensor,
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attention_mask: torch.Tensor,
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padding_mask: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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) -> torch.Tensor:
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# TODO: Modify docs.
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"""Forward pass for training and validation mode.
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@ -620,17 +609,16 @@ class EmformerAttention(nn.Module):
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D: embedding dimension;
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R: length of the hard-copied right contexts;
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U: length of full utterance;
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S: length of summary vectors;
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M: length of memory vectors.
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It computes a `big` attention matrix on full utterance and
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then utilizes a pre-computed mask to simulate chunk-wise attention.
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It concatenates three blocks: hard-copied right contexts,
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full utterance, and summary vectors, as a `big` block,
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and full utterance, as a `big` block,
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to compute the query tensor:
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query = [right_context, utterance, summary],
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with length Q = R + U + S.
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query = [right_context, utterance],
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with length Q = R + U.
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It concatenates the three blocks: memory vectors,
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hard-copied right contexts, and full utterance as another `big` block,
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to compute the key and value tensors:
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@ -644,10 +632,8 @@ class EmformerAttention(nn.Module):
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r_i: right context that c_i can use;
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l_i: left context that c_i can use;
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m_i: past memory vectors from previous layer that c_i can use;
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s_i: summary vector of c_i;
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The target chunk-wise attention is:
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c_i, r_i (in query) -> l_i, c_i, r_i, m_i (in key);
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s_i (in query) -> l_i, c_i, r_i (in key).
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c_i, r_i (in query) -> l_i, c_i, r_i, m_i (in key)
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Args:
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utterance (torch.Tensor):
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@ -655,9 +641,6 @@ class EmformerAttention(nn.Module):
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right_context (torch.Tensor):
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Hard-copied right context frames, with shape (R, B, D),
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where R = num_chunks * right_context_length
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summary (torch.Tensor):
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Summary elements with shape (S, B, D), where S = num_chunks.
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It is an empty tensor without using memory.
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memory (torch.Tensor):
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Memory elements, with shape (M, B, D), where M = num_chunks - 1.
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It is an empty tensor without using memory.
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@ -668,31 +651,22 @@ class EmformerAttention(nn.Module):
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Padding mask of key tensor, with shape (B, KV).
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Returns:
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A tuple containing 2 tensors:
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- output of right context and utterance, with shape (R + U, B, D).
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- memory output, with shape (M, B, D), where M = S - 1 or M = 0.
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Output of right context and utterance, with shape (R + U, B, D).
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"""
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(
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output_right_context_utterance,
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output_memory,
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_,
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_,
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) = self._forward_impl(
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output_right_context_utterance, _, _ = self._forward_impl(
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utterance,
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right_context,
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summary,
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memory,
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attention_mask,
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padding_mask=padding_mask,
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)
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return output_right_context_utterance, output_memory[:-1]
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return output_right_context_utterance
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@torch.jit.export
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def infer(
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self,
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utterance: torch.Tensor,
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right_context: torch.Tensor,
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summary: torch.Tensor,
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memory: torch.Tensor,
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left_context_key: torch.Tensor,
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left_context_val: torch.Tensor,
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@ -705,13 +679,12 @@ class EmformerAttention(nn.Module):
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R: length of right context;
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U: length of utterance, i.e., current chunk;
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L: length of cached left context;
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S: length of summary vectors, S = 1;
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M: length of cached memory vectors.
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It concatenates the right context, utterance (i.e., current chunk)
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and summary vector of current chunk, to compute the query tensor:
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query = [right_context, utterance, summary],
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with length Q = R + U + S.
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It concatenates the right context and utterance (i.e., current chunk)
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of current chunk, to compute the query tensor:
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query = [right_context, utterance],
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with length Q = R + U.
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It concatenates the memory vectors, right context, left context, and
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current chunk, to compute the key and value tensors:
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key & value = [memory, right_context, left_context, utterance],
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@ -719,8 +692,7 @@ class EmformerAttention(nn.Module):
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The chunk-wise attention is:
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chunk, right context (in query) ->
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left context, chunk, right context, memory vectors (in key);
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summary (in query) -> left context, chunk, right context (in key).
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left context, chunk, right context, memory vectors (in key).
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Args:
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utterance (torch.Tensor):
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@ -728,8 +700,6 @@ class EmformerAttention(nn.Module):
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right_context (torch.Tensor):
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Right context frames, with shape (R, B, D),
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where R = right_context_length.
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summary (torch.Tensor):
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Summary vector with shape (1, B, D), or empty tensor.
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memory (torch.Tensor):
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Memory vectors, with shape (M, B, D), or empty tensor.
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left_context_key (torch,Tensor):
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@ -744,7 +714,6 @@ class EmformerAttention(nn.Module):
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Returns:
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A tuple containing 4 tensors:
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- output of right context and utterance, with shape (R + U, B, D).
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- memory output, with shape (1, B, D) or (0, B, D).
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- attention key of left context and utterance, which would be cached
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for next computation, with shape (L + U, B, D).
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- attention value of left context and utterance, which would be
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@ -753,28 +722,19 @@ class EmformerAttention(nn.Module):
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U = utterance.size(0)
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R = right_context.size(0)
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L = left_context_key.size(0)
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S = summary.size(0)
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M = memory.size(0)
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# TODO: move it outside
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# query = [right context, utterance, summary]
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Q = R + U + S
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# query = [right context, utterance]
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Q = R + U
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# key, value = [memory, right context, left context, uttrance]
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KV = M + R + L + U
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attention_mask = torch.zeros(Q, KV).to(
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dtype=torch.bool, device=utterance.device
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)
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# disallow attention bettween the summary vector with the memory bank
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attention_mask[-1, :M] = True
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(
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output_right_context_utterance,
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output_memory,
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key,
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value,
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) = self._forward_impl(
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output_right_context_utterance, key, value = self._forward_impl(
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utterance,
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right_context,
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summary,
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memory,
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attention_mask,
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padding_mask=padding_mask,
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@ -783,7 +743,6 @@ class EmformerAttention(nn.Module):
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)
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return (
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output_right_context_utterance,
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output_memory,
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key[M + R :],
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value[M + R :],
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)
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@ -938,49 +897,46 @@ class EmformerEncoderLayer(nn.Module):
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self,
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right_context_utterance: torch.Tensor,
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R: int,
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memory: torch.Tensor,
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attention_mask: torch.Tensor,
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padding_mask: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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) -> torch.Tensor:
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"""Apply attention module in training and validation mode."""
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utterance = right_context_utterance[R:]
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right_context = right_context_utterance[:R]
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if self.use_memory:
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summary = self.summary_op(utterance.permute(1, 2, 0)).permute(
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memory = self.summary_op(utterance.permute(1, 2, 0)).permute(
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2, 0, 1
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)
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)[:-1, :, :]
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else:
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summary = torch.empty(0).to(
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memory = torch.empty(0).to(
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dtype=utterance.dtype, device=utterance.device
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)
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output_right_context_utterance, output_memory = self.attention(
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output_right_context_utterance = self.attention(
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utterance=utterance,
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right_context=right_context,
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summary=summary,
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memory=memory,
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attention_mask=attention_mask,
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padding_mask=padding_mask,
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)
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return output_right_context_utterance, output_memory
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return output_right_context_utterance
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def _apply_attention_module_infer(
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self,
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right_context_utterance: torch.Tensor,
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R: int,
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memory: torch.Tensor,
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attn_cache: List[torch.Tensor],
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padding_mask: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]:
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) -> Tuple[torch.Tensor, List[torch.Tensor]]:
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"""Apply attention module in inference mode.
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1) Unpack cached states including:
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- memory from previous chunks in the lower layer;
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- memory from previous chunks;
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- attention key and value of left context from preceding
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chunk's compuation;
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2) Apply attention computation;
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3) Update cached attention states including:
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- output memory of current chunk in the lower layer;
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- memory of current chunk;
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- attention key and value in current chunk's computation, which would
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be resued in next chunk's computation.
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"""
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@ -992,23 +948,20 @@ class EmformerEncoderLayer(nn.Module):
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left_context_val = attn_cache[2]
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if self.use_memory:
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summary = self.summary_op(utterance.permute(1, 2, 0)).permute(
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memory = self.summary_op(utterance.permute(1, 2, 0)).permute(
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2, 0, 1
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)
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summary = summary[:1]
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)[:1, :, :]
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else:
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summary = torch.empty(0).to(
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memory = torch.empty(0).to(
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dtype=utterance.dtype, device=utterance.device
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)
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(
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output_right_context_utterance,
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output_memory,
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next_key,
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next_val,
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) = self.attention.infer(
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utterance=utterance,
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right_context=right_context,
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summary=summary,
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memory=pre_memory,
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left_context_key=left_context_key,
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left_context_val=left_context_val,
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@ -1017,17 +970,16 @@ class EmformerEncoderLayer(nn.Module):
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attn_cache = self._update_attn_cache(
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next_key, next_val, memory, attn_cache
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)
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return output_right_context_utterance, output_memory, attn_cache
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return output_right_context_utterance, attn_cache
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def forward(
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self,
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utterance: torch.Tensor,
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right_context: torch.Tensor,
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memory: torch.Tensor,
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attention_mask: torch.Tensor,
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padding_mask: Optional[torch.Tensor] = None,
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warmup: float = 1.0,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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) -> Tuple[torch.Tensor, torch.Tensor]:
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r"""Forward pass for training and validation mode.
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B: batch size;
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@ -1041,20 +993,16 @@ class EmformerEncoderLayer(nn.Module):
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Utterance frames, with shape (U, B, D).
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right_context (torch.Tensor):
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Right context frames, with shape (R, B, D).
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memory (torch.Tensor):
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Memory elements, with shape (M, B, D).
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It is an empty tensor without using memory.
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attention_mask (torch.Tensor):
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Attention mask for underlying attention module,
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with shape (Q, KV), where Q = R + U + S, KV = M + R + U.
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with shape (Q, KV), where Q = R + U, KV = M + R + U.
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padding_mask (torch.Tensor):
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Padding mask of ker tensor, with shape (B, KV).
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Returns:
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A tuple containing 3 tensors:
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A tuple containing 2 tensors:
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- output utterance, with shape (U, B, D).
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- output right context, with shape (R, B, D).
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- output memory, with shape (M, B, D).
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"""
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R = right_context.size(0)
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src = torch.cat([right_context, utterance])
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@ -1076,8 +1024,8 @@ class EmformerEncoderLayer(nn.Module):
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src = src + self.dropout(self.feed_forward_macaron(src))
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# emformer attention module
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src_att, output_memory = self._apply_attention_module_forward(
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src, R, memory, attention_mask, padding_mask=padding_mask
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src_att = self._apply_attention_module_forward(
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src, R, attention_mask, padding_mask=padding_mask
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)
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src = src + self.dropout(src_att)
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@ -1095,24 +1043,17 @@ class EmformerEncoderLayer(nn.Module):
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output_utterance = src[R:]
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output_right_context = src[:R]
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return output_utterance, output_right_context, output_memory
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return output_utterance, output_right_context
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@torch.jit.export
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def infer(
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self,
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utterance: torch.Tensor,
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right_context: torch.Tensor,
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memory: torch.Tensor,
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attn_cache: List[torch.Tensor],
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conv_cache: torch.Tensor,
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padding_mask: Optional[torch.Tensor] = None,
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) -> Tuple[
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torch.Tensor,
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torch.Tensor,
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torch.Tensor,
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List[torch.Tensor],
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torch.Tensor,
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]:
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) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor], torch.Tensor]:
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"""Forward pass for inference.
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B: batch size;
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@ -1126,8 +1067,6 @@ class EmformerEncoderLayer(nn.Module):
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Utterance frames, with shape (U, B, D).
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right_context (torch.Tensor):
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Right context frames, with shape (R, B, D).
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memory (torch.Tensor):
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Memory elements, with shape (M, B, D).
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attn_cache (List[torch.Tensor]):
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Cached attention tensors generated in preceding computation,
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including memory, key and value of left context.
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@ -1140,9 +1079,8 @@ class EmformerEncoderLayer(nn.Module):
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(Tensor, Tensor, List[torch.Tensor], Tensor):
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- output utterance, with shape (U, B, D);
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- output right_context, with shape (R, B, D);
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- output memory, with shape (1, B, D) or (0, B, D).
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- output state.
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- updated conv_cache.
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- output attention cache;
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- output convolution cache.
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"""
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R = right_context.size(0)
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src = torch.cat([right_context, utterance])
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@ -1151,12 +1089,8 @@ class EmformerEncoderLayer(nn.Module):
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src = src + self.dropout(self.feed_forward_macaron(src))
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# emformer attention module
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(
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src_att,
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output_memory,
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attn_cache,
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) = self._apply_attention_module_infer(
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src, R, memory, attn_cache, padding_mask=padding_mask
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src_att, attn_cache = self._apply_attention_module_infer(
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src, R, attn_cache, padding_mask=padding_mask
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)
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src = src + self.dropout(src_att)
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@ -1174,7 +1108,6 @@ class EmformerEncoderLayer(nn.Module):
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return (
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output_utterance,
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output_right_context,
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output_memory,
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attn_cache,
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conv_cache,
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)
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@ -1253,11 +1186,6 @@ class EmformerEncoder(nn.Module):
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super().__init__()
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self.use_memory = memory_size > 0
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self.init_memory_op = nn.AvgPool1d(
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kernel_size=chunk_length,
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stride=chunk_length,
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ceil_mode=True,
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)
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self.emformer_layers = nn.ModuleList(
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[
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@ -1358,16 +1286,15 @@ class EmformerEncoder(nn.Module):
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R: length of hard-copied right contexts;
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U: length of full utterance;
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S: length of summary vectors;
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M: length of memory vectors;
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Q: length of attention query;
|
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KV: length of attention key and value.
|
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The shape of attention mask is (Q, KV).
|
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If self.use_memory is `True`:
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query = [right_context, utterance, summary];
|
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query = [right_context, utterance];
|
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key, value = [memory, right_context, utterance];
|
||||
Q = R + U + S, KV = M + R + U.
|
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Q = R + U, KV = M + R + U.
|
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Otherwise:
|
||||
query = [right_context, utterance]
|
||||
key, value = [right_context, utterance]
|
||||
@ -1378,17 +1305,14 @@ class EmformerEncoder(nn.Module):
|
||||
r_i: right context that c_i can use;
|
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l_i: left context that c_i can use;
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||||
m_i: past memory vectors from previous layer that c_i can use;
|
||||
s_i: summary vector of c_i.
|
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The target chunk-wise attention is:
|
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c_i, r_i (in query) -> l_i, c_i, r_i, m_i (in key);
|
||||
s_i (in query) -> l_i, c_i, r_i (in key).
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||||
c_i, r_i (in query) -> l_i, c_i, r_i, m_i (in key).
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||||
"""
|
||||
U = utterance.size(0)
|
||||
num_chunks = math.ceil(U / self.chunk_length)
|
||||
|
||||
right_context_mask = []
|
||||
utterance_mask = []
|
||||
summary_mask = []
|
||||
|
||||
if self.use_memory:
|
||||
num_cols = 9
|
||||
@ -1397,9 +1321,6 @@ class EmformerEncoder(nn.Module):
|
||||
right_context_utterance_cols_mask = [
|
||||
idx in [1, 4, 7] for idx in range(num_cols)
|
||||
]
|
||||
# summary attends to right context, utterance
|
||||
summary_cols_mask = [idx in [4, 7] for idx in range(num_cols)]
|
||||
masks_to_concat = [right_context_mask, utterance_mask, summary_mask]
|
||||
else:
|
||||
num_cols = 6
|
||||
# right context and utterance both attend to right context and
|
||||
@ -1407,7 +1328,6 @@ class EmformerEncoder(nn.Module):
|
||||
right_context_utterance_cols_mask = [
|
||||
idx in [1, 4] for idx in range(num_cols)
|
||||
]
|
||||
summary_cols_mask = None
|
||||
masks_to_concat = [right_context_mask, utterance_mask]
|
||||
|
||||
for chunk_idx in range(num_chunks):
|
||||
@ -1432,12 +1352,6 @@ class EmformerEncoder(nn.Module):
|
||||
)
|
||||
utterance_mask.append(utterance_mask_block)
|
||||
|
||||
if summary_cols_mask is not None:
|
||||
summary_mask_block = _gen_attention_mask_block(
|
||||
col_widths, summary_cols_mask, 1, utterance.device
|
||||
)
|
||||
summary_mask.append(summary_mask_block)
|
||||
|
||||
attention_mask = (
|
||||
1 - torch.cat([torch.cat(mask) for mask in masks_to_concat])
|
||||
).to(torch.bool)
|
||||
@ -1473,23 +1387,15 @@ class EmformerEncoder(nn.Module):
|
||||
utterance = x[:U]
|
||||
output_lengths = torch.clamp(lengths - self.right_context_length, min=0)
|
||||
attention_mask = self._gen_attention_mask(utterance)
|
||||
memory = (
|
||||
self.init_memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)[
|
||||
:-1
|
||||
]
|
||||
if self.use_memory
|
||||
else torch.empty(0).to(dtype=x.dtype, device=x.device)
|
||||
)
|
||||
padding_mask = make_pad_mask(
|
||||
memory.size(0) + right_context.size(0) + output_lengths
|
||||
)
|
||||
|
||||
M = right_context.size(0) // self.chunk_length - 1
|
||||
padding_mask = make_pad_mask(M + right_context.size(0) + output_lengths)
|
||||
|
||||
output = utterance
|
||||
for layer in self.emformer_layers:
|
||||
output, right_context, memory = layer(
|
||||
output, right_context = layer(
|
||||
output,
|
||||
right_context,
|
||||
memory,
|
||||
attention_mask,
|
||||
padding_mask=padding_mask,
|
||||
warmup=warmup,
|
||||
@ -1525,7 +1431,6 @@ class EmformerEncoder(nn.Module):
|
||||
right_context at the end.
|
||||
states (List[torch.Tensor, List[List[torch.Tensor]], List[torch.Tensor]]: # noqa
|
||||
Cached states containing:
|
||||
- past_lens: number of past frames for each sample in batch
|
||||
- attn_caches: attention states from preceding chunk's computation,
|
||||
where each element corresponds to each emformer layer
|
||||
- conv_caches: left context for causal convolution, where each
|
||||
@ -1571,11 +1476,6 @@ class EmformerEncoder(nn.Module):
|
||||
right_context = x[-self.right_context_length :]
|
||||
utterance = x[: -self.right_context_length]
|
||||
output_lengths = torch.clamp(lengths - self.right_context_length, min=0)
|
||||
memory = (
|
||||
self.init_memory_op(utterance.permute(1, 2, 0)).permute(2, 0, 1)
|
||||
if self.use_memory
|
||||
else torch.empty(0).to(dtype=x.dtype, device=x.device)
|
||||
)
|
||||
|
||||
# calcualte padding mask to mask out initial zero caches
|
||||
chunk_mask = make_pad_mask(output_lengths).to(x.device)
|
||||
@ -1611,13 +1511,11 @@ class EmformerEncoder(nn.Module):
|
||||
(
|
||||
output,
|
||||
right_context,
|
||||
memory,
|
||||
output_attn_cache,
|
||||
output_conv_cache,
|
||||
) = layer.infer(
|
||||
output,
|
||||
right_context,
|
||||
memory,
|
||||
padding_mask=padding_mask,
|
||||
attn_cache=attn_caches[layer_idx],
|
||||
conv_cache=conv_caches[layer_idx],
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user