update vector ctc prefix score

deepspeech/decoders/scores/ctc.py

@@ -4,7 +4,7 @@ import numpy as np
 import paddle
 
 from .ctc_prefix_score import CTCPrefixScore
-from .ctc_prefix_score import CTCPrefixScoreTH
+from .ctc_prefix_score import CTCPrefixScorePD
 from .scorer_interface import BatchPartialScorerInterface
 
 
@@ -34,7 +34,7 @@ class CTCPrefixScorer(BatchPartialScorerInterface):
 
         """
         logp = self.ctc.log_softmax(x.unsqueeze(0)).squeeze(0).numpy()
-        # TODO(karita): use CTCPrefixScoreTH
+        # TODO(karita): use CTCPrefixScorePD
         self.impl = CTCPrefixScore(logp, 0, self.eos, np)
         return 0, self.impl.initial_state()
 
@@ -54,7 +54,7 @@ class CTCPrefixScorer(BatchPartialScorerInterface):
             if len(state) == 2:  # for CTCPrefixScore
                 sc, st = state
                 return sc[i], st[i]
-            else:  # for CTCPrefixScoreTH (need new_id > 0)
+            else:  # for CTCPrefixScorePD (need new_id > 0)
                 r, log_psi, f_min, f_max, scoring_idmap = state
                 s = log_psi[i, new_id].expand(log_psi.size(1))
                 if scoring_idmap is not None:
@@ -96,7 +96,7 @@ class CTCPrefixScorer(BatchPartialScorerInterface):
         """
         logp = self.ctc.log_softmax(x.unsqueeze(0))  # assuming batch_size = 1
         xlen = paddle.to_tensor([logp.size(1)])
-        self.impl = CTCPrefixScoreTH(logp, xlen, 0, self.eos)
+        self.impl = CTCPrefixScorePD(logp, xlen, 0, self.eos)
         return None
 
     def batch_score_partial(self, y, ids, state, x):

deepspeech/decoders/scores/ctc_prefix_score.py

@@ -3,13 +3,13 @@
 # Copyright 2018 Mitsubishi Electric Research Labs (Takaaki Hori)
 #  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
 
-import torch
+import paddle
 
 import numpy as np
 import six
 
 
-class CTCPrefixScoreTH():
+class CTCPrefixScorePD():
     """Batch processing of CTCPrefixScore
 
     which is based on Algorithm 2 in WATANABE et al.
@@ -23,8 +23,10 @@ class CTCPrefixScoreTH():
     def __init__(self, x, xlens, blank, eos, margin=0):
         """Construct CTC prefix scorer
 
-        :param torch.Tensor x: input label posterior sequences (B, T, O)
-        :param torch.Tensor xlens: input lengths (B,)
+        `margin` is M in eq.(22,23)
+
+        :param paddle.Tensor x: input label posterior sequences (B, T, O)
+        :param paddle.Tensor xlens: input lengths (B,)
         :param int blank: blank label id
         :param int eos: end-of-sequence id
         :param int margin: margin parameter for windowing (0 means no windowing)
@@ -38,11 +40,8 @@ class CTCPrefixScoreTH():
         self.input_length = x.size(1)
         self.odim = x.size(2)
         self.dtype = x.dtype
-        self.device = (
-            torch.device("cuda:%d" % x.get_device())
-            if x.is_cuda
-            else torch.device("cpu")
-        )
+        self.device = x.place
+
         # Pad the rest of posteriors in the batch
         # TODO(takaaki-hori): need a better way without for-loops
         for i, l in enumerate(xlens):
@@ -50,20 +49,21 @@ class CTCPrefixScoreTH():
                 x[i, l:, :] = self.logzero
                 x[i, l:, blank] = 0
         # Reshape input x
-        xn = x.transpose(0, 1)  # (B, T, O) -> (T, B, O)
-        xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim)
-        self.x = torch.stack([xn, xb])  # (2, T, B, O)
-        self.end_frames = torch.as_tensor(xlens) - 1
+        xn = x.transpose([1, 0, 2])  # (B, T, O) -> (T, B, O)
+        xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim) # (T,B,O)
+        self.x = paddle.stack([xn, xb])  # (2, T, B, O)
+        self.end_frames = paddle.to_tensor(xlens) - 1 # (B,)
 
         # Setup CTC windowing
         self.margin = margin
         if margin > 0:
-            self.frame_ids = torch.arange(
-                self.input_length, dtype=self.dtype, device=self.device
-            )
+            self.frame_ids = paddle.arange(self.input_length, dtype=self.dtype)
         # Base indices for index conversion
-        self.idx_bh = None
-        self.idx_b = torch.arange(self.batch, device=self.device)
+        # B idx, hyp idx. shape (B*W, 1)
+        self.idx_bh = None 
+        # B idx. shape (B,)
+        self.idx_b = paddle.arange(self.batch, place=self.device)
+        # B idx, O idx. shape (B, 1)
         self.idx_bo = (self.idx_b * self.odim).unsqueeze(1)
 
     def __call__(self, y, state, scoring_ids=None, att_w=None):
@@ -71,8 +71,8 @@ class CTCPrefixScoreTH():
 
         :param list y: prefix label sequences
         :param tuple state: previous CTC state
-        :param torch.Tensor pre_scores: scores for pre-selection of hypotheses (BW, O)
-        :param torch.Tensor att_w: attention weights to decide CTC window
+        :param paddle.Tensor scoring_ids: selected next ids to score (BW, O'), O' <= O
+        :param paddle.Tensor att_w: attention weights to decide CTC window
         :return new_state, ctc_local_scores (BW, O)
         """
         output_length = len(y[0]) - 1  # ignore sos
@@ -82,56 +82,53 @@ class CTCPrefixScoreTH():
         self.scoring_num = scoring_ids.size(-1) if scoring_ids is not None else 0
         # prepare state info
         if state is None:
-            r_prev = torch.full(
+            r_prev = paddle.full(
                 (self.input_length, 2, self.batch, n_hyps),
                 self.logzero,
                 dtype=self.dtype,
-                device=self.device,
-            )
-            r_prev[:, 1] = torch.cumsum(self.x[0, :, :, self.blank], 0).unsqueeze(2)
-            r_prev = r_prev.view(-1, 2, n_bh)
-            s_prev = 0.0
-            f_min_prev = 0
-            f_max_prev = 1
+            ) # (T, 2, B, W)
+            r_prev[:, 1] = paddle.cumsum(self.x[0, :, :, self.blank], 0).unsqueeze(2)
+            r_prev = r_prev.view(-1, 2, n_bh) # (T, 2, BW)
+            s_prev = 0.0  # score
+            f_min_prev = 0 # eq. 22-23
+            f_max_prev = 1 # eq. 22-23
         else:
             r_prev, s_prev, f_min_prev, f_max_prev = state
 
         # select input dimensions for scoring
         if self.scoring_num > 0:
-            scoring_idmap = torch.full(
-                (n_bh, self.odim), -1, dtype=torch.long, device=self.device
-            )
+            # (BW, O)
+            scoring_idmap = paddle.full((n_bh, self.odim), -1, dtype=paddle.long)
             snum = self.scoring_num
             if self.idx_bh is None or n_bh > len(self.idx_bh):
-                self.idx_bh = torch.arange(n_bh, device=self.device).view(-1, 1)
-            scoring_idmap[self.idx_bh[:n_bh], scoring_ids] = torch.arange(
-                snum, device=self.device
-            )
+                self.idx_bh = paddle.arange(n_bh).view(-1, 1) # (BW, 1)
+            scoring_idmap[self.idx_bh[:n_bh], scoring_ids] = paddle.arange(snum)
             scoring_idx = (
-                scoring_ids + self.idx_bo.repeat(1, n_hyps).view(-1, 1)
-            ).view(-1)
-            x_ = torch.index_select(
-                self.x.view(2, -1, self.batch * self.odim), 2, scoring_idx
+                scoring_ids + self.idx_bo.repeat(1, n_hyps).view(-1, 1) # (BW,1)
+            ).view(-1) # (BWO)
+            # x_ shape (2, T, B*W, O)
+            x_ = paddle.index_select(
+                self.x.view(2, -1, self.batch * self.odim), scoring_idx, 2
             ).view(2, -1, n_bh, snum)
         else:
             scoring_ids = None
             scoring_idmap = None
             snum = self.odim
+            # x_ shape (2, T, B*W, O)
             x_ = self.x.unsqueeze(3).repeat(1, 1, 1, n_hyps, 1).view(2, -1, n_bh, snum)
 
         # new CTC forward probs are prepared as a (T x 2 x BW x S) tensor
         # that corresponds to r_t^n(h) and r_t^b(h) in a batch.
-        r = torch.full(
+        r = paddle.full(
             (self.input_length, 2, n_bh, snum),
             self.logzero,
             dtype=self.dtype,
-            device=self.device,
         )
         if output_length == 0:
             r[0, 0] = x_[0, 0]
 
-        r_sum = torch.logsumexp(r_prev, 1)
-        log_phi = r_sum.unsqueeze(2).repeat(1, 1, snum)
+        r_sum = paddle.logsumexp(r_prev, 1) #(T,BW)
+        log_phi = r_sum.unsqueeze(2).repeat(1, 1, snum) # (T, BW, O)
         if scoring_ids is not None:
             for idx in range(n_bh):
                 pos = scoring_idmap[idx, last_ids[idx]]
@@ -143,40 +140,39 @@ class CTCPrefixScoreTH():
 
         # decide start and end frames based on attention weights
         if att_w is not None and self.margin > 0:
-            f_arg = torch.matmul(att_w, self.frame_ids)
+            f_arg = paddle.matmul(att_w, self.frame_ids)
             f_min = max(int(f_arg.min().cpu()), f_min_prev)
             f_max = max(int(f_arg.max().cpu()), f_max_prev)
             start = min(f_max_prev, max(f_min - self.margin, output_length, 1))
             end = min(f_max + self.margin, self.input_length)
         else:
             f_min = f_max = 0
+            # if one frame one out, the output_length is the eating frame num now.
             start = max(output_length, 1)
             end = self.input_length
 
         # compute forward probabilities log(r_t^n(h)) and log(r_t^b(h))
         for t in range(start, end):
-            rp = r[t - 1]
-            rr = torch.stack([rp[0], log_phi[t - 1], rp[0], rp[1]]).view(
+            rp = r[t - 1] # (2 x BW x O') 
+            rr = paddle.stack([rp[0], log_phi[t - 1], rp[0], rp[1]]).view(
                 2, 2, n_bh, snum
-            )
-            r[t] = torch.logsumexp(rr, 1) + x_[:, t]
+            ) # (2,2,BW,O')
+            r[t] = paddle.logsumexp(rr, 1) + x_[:, t]
 
         # compute log prefix probabilities log(psi)
-        log_phi_x = torch.cat((log_phi[0].unsqueeze(0), log_phi[:-1]), dim=0) + x_[0]
+        log_phi_x = paddle.concat((log_phi[0].unsqueeze(0), log_phi[:-1]), axis=0) + x_[0]
         if scoring_ids is not None:
-            log_psi = torch.full(
-                (n_bh, self.odim), self.logzero, dtype=self.dtype, device=self.device
-            )
-            log_psi_ = torch.logsumexp(
-                torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0),
-                dim=0,
+            log_psi = paddle.full((n_bh, self.odim), self.logzero, dtype=self.dtype)
+            log_psi_ = paddle.logsumexp(
+                paddle.concat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), axis=0),
+                axis=0,
             )
             for si in range(n_bh):
                 log_psi[si, scoring_ids[si]] = log_psi_[si]
         else:
-            log_psi = torch.logsumexp(
-                torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0),
-                dim=0,
+            log_psi = paddle.logsumexp(
+                paddle.concat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), axis=0),
+                axis=0,
             )
 
         for si in range(n_bh):
@@ -200,7 +196,7 @@ class CTCPrefixScoreTH():
         n_hyps = n_bh // self.batch
         vidx = (best_ids + (self.idx_b * (n_hyps * self.odim)).view(-1, 1)).view(-1)
         # select hypothesis scores
-        s_new = torch.index_select(s.view(-1), 0, vidx)
+        s_new = paddle.index_select(s.view(-1), vidx, 0)
         s_new = s_new.view(-1, 1).repeat(1, self.odim).view(n_bh, self.odim)
         # convert ids to BHS space (S: scoring_num)
         if scoring_idmap is not None:
@@ -208,14 +204,14 @@ class CTCPrefixScoreTH():
             hyp_idx = (best_ids // self.odim + (self.idx_b * n_hyps).view(-1, 1)).view(
                 -1
             )
-            label_ids = torch.fmod(best_ids, self.odim).view(-1)
+            label_ids = paddle.fmod(best_ids, self.odim).view(-1)
             score_idx = scoring_idmap[hyp_idx, label_ids]
             score_idx[score_idx == -1] = 0
             vidx = score_idx + hyp_idx * snum
         else:
             snum = self.odim
         # select forward probabilities
-        r_new = torch.index_select(r.view(-1, 2, n_bh * snum), 2, vidx).view(
+        r_new = paddle.index_select(r.view(-1, 2, n_bh * snum), vidx, 2).view(
             -1, 2, n_bh
         )
         return r_new, s_new, f_min, f_max
@@ -223,7 +219,7 @@ class CTCPrefixScoreTH():
     def extend_prob(self, x):
         """Extend CTC prob.
 
-        :param torch.Tensor x: input label posterior sequences (B, T, O)
+        :param paddle.Tensor x: input label posterior sequences (B, T, O)
         """
 
         if self.x.shape[1] < x.shape[1]:  # self.x (2,T,B,O); x (B,T,O)
@@ -235,12 +231,12 @@ class CTCPrefixScoreTH():
                     x[i, l:, :] = self.logzero
                     x[i, l:, self.blank] = 0
             tmp_x = self.x
-            xn = x.transpose(0, 1)  # (B, T, O) -> (T, B, O)
+            xn = x.transpose([1, 0, 2])  # (B, T, O) -> (T, B, O)
             xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim)
-            self.x = torch.stack([xn, xb])  # (2, T, B, O)
+            self.x = paddle.stack([xn, xb])  # (2, T, B, O)
             self.x[:, : tmp_x.shape[1], :, :] = tmp_x
             self.input_length = x.size(1)
-            self.end_frames = torch.as_tensor(xlens) - 1
+            self.end_frames = paddle.to_tensor(xlens) - 1
 
     def extend_state(self, state):
         """Compute CTC prefix state.
@@ -256,15 +252,14 @@ class CTCPrefixScoreTH():
         else:
             r_prev, s_prev, f_min_prev, f_max_prev = state
 
-            r_prev_new = torch.full(
+            r_prev_new = paddle.full(
                 (self.input_length, 2),
                 self.logzero,
                 dtype=self.dtype,
-                device=self.device,
             )
             start = max(r_prev.shape[0], 1)
             r_prev_new[0:start] = r_prev
-            for t in six.moves.range(start, self.input_length):
+            for t in range(start, self.input_length):
                 r_prev_new[t, 1] = r_prev_new[t - 1, 1] + self.x[0, t, :, self.blank]
 
             return (r_prev_new, s_prev, f_min_prev, f_max_prev)
@@ -285,7 +280,7 @@ class CTCPrefixScore():
         self.blank = blank
         self.eos = eos
         self.input_length = len(x)
-        self.x = x
+        self.x = x # (T, O)
 
     def initial_state(self):
         """Obtain an initial CTC state
@@ -295,6 +290,7 @@ class CTCPrefixScore():
         # initial CTC state is made of a frame x 2 tensor that corresponds to
         # r_t^n(<sos>) and r_t^b(<sos>), where 0 and 1 of axis=1 represent
         # superscripts n and b (non-blank and blank), respectively.
+        # r shape (T, 2)
         r = self.xp.full((self.input_length, 2), self.logzero, dtype=np.float32)
         r[0, 1] = self.x[0, self.blank]
         for i in six.moves.range(1, self.input_length):
@@ -313,6 +309,7 @@ class CTCPrefixScore():
         output_length = len(y) - 1  # ignore sos
         # new CTC states are prepared as a frame x (n or b) x n_labels tensor
         # that corresponds to r_t^n(h) and r_t^b(h).
+        # r shape (T, 2, n_labels)
         r = self.xp.ndarray((self.input_length, 2, len(cs)), dtype=np.float32)
         xs = self.x[:, cs]
         if output_length == 0:
@@ -356,4 +353,5 @@ class CTCPrefixScore():
 
         # return the log prefix probability and CTC states, where the label axis
         # of the CTC states is moved to the first axis to slice it easily
+        # log_psi shape (n_labels,), state shape (n_labels, T, 2)
         return log_psi, self.xp.rollaxis(r, 2)