Merge branch 'master' of github.com:calico/basenji

basenji/seqnn.py

@@ -382,9 +382,265 @@ def get_conv_weights(self, conv_layer_i=0):
     weights = np.transpose(weights, [2,1,0])
     return weights
 
+  def gradients(self, seq_1hot, head_i=None, target_slice=None, pos_slice=None, pos_mask=None, pos_slice_denom=None, pos_mask_denom=None, chunk_size=None, batch_size=1, track_scale=1., track_transform=1., clip_soft=None, pseudo_count=0., no_transform=False, use_mean=False, use_ratio=False, use_logodds=False, subtract_avg=True, input_gate=True, smooth_grad=False, n_samples=5, sample_prob=0.875, dtype='float16'):
+    """ Compute input gradients for sequences (GPU-friendly). """
+
+    # start time
+    t0 = time.time()
+
+    # choose model
+    if self.ensemble is not None:
+      model = self.ensemble
+    elif head_i is not None:
+      model = self.models[head_i]
+    else:
+      model = self.model
+
+    # verify tensor shape(s)
+    seq_1hot = seq_1hot.astype('float32')
+    target_slice = np.array(target_slice).astype('int32')
+    pos_slice = np.array(pos_slice).astype('int32')
+
+    # convert constants to tf tensors
+    track_scale = tf.constant(track_scale, dtype=tf.float32)
+    track_transform = tf.constant(track_transform, dtype=tf.float32)
+    if clip_soft is not None :
+        clip_soft = tf.constant(clip_soft, dtype=tf.float32)
+    pseudo_count = tf.constant(pseudo_count, dtype=tf.float32)
+
+    if pos_mask is not None :
+      pos_mask = np.array(pos_mask).astype('float32')
+
+    if use_ratio and pos_slice_denom is not None :
+      pos_slice_denom = np.array(pos_slice_denom).astype('int32')
+
+      if pos_mask_denom is not None :
+        pos_mask_denom = np.array(pos_mask_denom).astype('float32')
+
+    if len(seq_1hot.shape) < 3:
+      seq_1hot = seq_1hot[None, ...]
+
+    if len(target_slice.shape) < 2:
+      target_slice = target_slice[None, ...]
+
+    if len(pos_slice.shape) < 2:
+      pos_slice = pos_slice[None, ...]
+
+    if pos_mask is not None and len(pos_mask.shape) < 2:
+      pos_mask = pos_mask[None, ...]
+
+    if use_ratio and pos_slice_denom is not None and len(pos_slice_denom.shape) < 2:
+      pos_slice_denom = pos_slice_denom[None, ...]
+
+      if pos_mask_denom is not None and len(pos_mask_denom.shape) < 2:
+        pos_mask_denom = pos_mask_denom[None, ...]
+
+    # chunk parameters
+    num_chunks = 1
+    if chunk_size is None :
+      chunk_size = seq_1hot.shape[0]
+    else :
+      num_chunks = int(np.ceil(seq_1hot.shape[0] / chunk_size))
+
+    # loop over chunks
+    grad_chunks = []
+    for ci in range(num_chunks) :
+
+      # collect chunk
+      seq_1hot_chunk = seq_1hot[ci * chunk_size:(ci+1) * chunk_size, ...]
+      target_slice_chunk = target_slice[ci * chunk_size:(ci+1) * chunk_size, ...]
+      pos_slice_chunk = pos_slice[ci * chunk_size:(ci+1) * chunk_size, ...]
+
+      pos_mask_chunk = None
+      if pos_mask is not None :
+        pos_mask_chunk = pos_mask[ci * chunk_size:(ci+1) * chunk_size, ...]
+
+      pos_slice_denom_chunk = None
+      pos_mask_denom_chunk = None
+      if use_ratio and pos_slice_denom is not None :
+        pos_slice_denom_chunk = pos_slice_denom[ci * chunk_size:(ci+1) * chunk_size, ...]
+
+        if pos_mask_denom is not None :
+          pos_mask_denom_chunk = pos_mask_denom[ci * chunk_size:(ci+1) * chunk_size, ...]
+
+      actual_chunk_size = seq_1hot_chunk.shape[0]
+
+      # sample noisy (discrete) perturbations of the input pattern chunk
+      if smooth_grad :
+        seq_1hot_chunk_corrupted = np.repeat(np.copy(seq_1hot_chunk), n_samples, axis=0)
+
+        for example_ix in range(seq_1hot_chunk.shape[0]) :
+          for sample_ix in range(n_samples) :
+            corrupt_index = np.nonzero(np.random.rand(seq_1hot_chunk.shape[1]) >= sample_prob)[0]
+
+            rand_nt_index = np.random.choice([0, 1, 2, 3], size=(corrupt_index.shape[0],))
+
+            seq_1hot_chunk_corrupted[example_ix * n_samples + sample_ix, corrupt_index, :] = 0.
+            seq_1hot_chunk_corrupted[example_ix * n_samples + sample_ix, corrupt_index, rand_nt_index] = 1.
+
+        seq_1hot_chunk = seq_1hot_chunk_corrupted
+        target_slice_chunk = np.repeat(np.copy(target_slice_chunk), n_samples, axis=0)
+        pos_slice_chunk = np.repeat(np.copy(pos_slice_chunk), n_samples, axis=0)
+
+        if pos_mask is not None :
+          pos_mask_chunk = np.repeat(np.copy(pos_mask_chunk), n_samples, axis=0)
+
+        if use_ratio and pos_slice_denom is not None :
+          pos_slice_denom_chunk = np.repeat(np.copy(pos_slice_denom_chunk), n_samples, axis=0)
+
+          if pos_mask_denom is not None :
+            pos_mask_denom_chunk = np.repeat(np.copy(pos_mask_denom_chunk), n_samples, axis=0)
+
+      # convert to tf tensors
+      seq_1hot_chunk = tf.convert_to_tensor(seq_1hot_chunk, dtype=tf.float32)
+      target_slice_chunk = tf.convert_to_tensor(target_slice_chunk, dtype=tf.int32)
+      pos_slice_chunk = tf.convert_to_tensor(pos_slice_chunk, dtype=tf.int32)
+
+      if pos_mask is not None :
+        pos_mask_chunk = tf.convert_to_tensor(pos_mask_chunk, dtype=tf.float32)
+
+      if use_ratio and pos_slice_denom is not None :
+        pos_slice_denom_chunk = tf.convert_to_tensor(pos_slice_denom_chunk, dtype=tf.int32)
+
+        if pos_mask_denom is not None :
+          pos_mask_denom_chunk = tf.convert_to_tensor(pos_mask_denom_chunk, dtype=tf.float32)
+
+      # batching parameters
+      num_batches = int(np.ceil(actual_chunk_size * (n_samples if smooth_grad else 1) / batch_size))
+
+      # loop over batches
+      grad_batches = []
+      for bi in range(num_batches) :
+
+        # collect batch
+        seq_1hot_batch = seq_1hot_chunk[bi * batch_size:(bi+1) * batch_size, ...]
+        target_slice_batch = target_slice_chunk[bi * batch_size:(bi+1) * batch_size, ...]
+        pos_slice_batch = pos_slice_chunk[bi * batch_size:(bi+1) * batch_size, ...]
+
+        pos_mask_batch = None
+        if pos_mask is not None :
+          pos_mask_batch = pos_mask_chunk[bi * batch_size:(bi+1) * batch_size, ...]
+
+        pos_slice_denom_batch = None
+        pos_mask_denom_batch = None
+        if use_ratio and pos_slice_denom is not None :
+          pos_slice_denom_batch = pos_slice_denom_chunk[bi * batch_size:(bi+1) * batch_size, ...]
+
+          if pos_mask_denom is not None :
+            pos_mask_denom_batch = pos_mask_denom_chunk[bi * batch_size:(bi+1) * batch_size, ...]
+
+        grad_batch = self.gradients_func(model, seq_1hot_batch, target_slice_batch, pos_slice_batch, pos_mask_batch, pos_slice_denom_batch, pos_mask_denom_batch, track_scale, track_transform, clip_soft, pseudo_count, no_transform, use_mean, use_ratio, use_logodds, subtract_avg, input_gate).numpy().astype(dtype)
+
+        grad_batches.append(grad_batch)
+
+      # concat gradient batches
+      grads = np.concatenate(grad_batches, axis=0)
+
+      # aggregate noisy gradient perturbations
+      if smooth_grad :
+        grads_smoothed = np.zeros((grads.shape[0] // n_samples, grads.shape[1], grads.shape[2]), dtype='float32')
+
+        for example_ix in range(grads_smoothed.shape[0]) :
+          for sample_ix in range(n_samples) :
+            grads_smoothed[example_ix, ...] += grads[example_ix * n_samples + sample_ix, ...]
+
+        grads = grads_smoothed / float(n_samples)
+        grads = grads.astype(dtype)
+
+      grad_chunks.append(grads)
+
+      # collect garbage
+      gc.collect()
+
+    # concat gradient chunks
+    grads = np.concatenate(grad_chunks, axis=0)
+
+    # aggregate and broadcast to original input pattern
+    if input_gate :
+      grads = np.sum(grads, axis=-1, keepdims=True) * seq_1hot
+
+    print('Completed gradient computation in %ds' % (time.time()-t0))
+
+    return grads
+
+  @tf.function
+  def gradients_func(self, model, seq_1hot, target_slice, pos_slice, pos_mask=None, pos_slice_denom=None, pos_mask_denom=True, track_scale=1., track_transform=1., clip_soft=None, pseudo_count=0., no_transform=False, use_mean=False, use_ratio=False, use_logodds=False, subtract_avg=True, input_gate=True):
+
+    with tf.GradientTape() as tape:
+      tape.watch(seq_1hot)
+
+      # predict
+      preds = tf.gather(model(seq_1hot, training=False), target_slice, axis=-1, batch_dims=1)
+
+      if not no_transform :
+
+        # undo scale
+        preds = preds / track_scale
+
+        # undo soft_clip
+        if clip_soft is not None :
+          preds = tf.where(preds > clip_soft, (preds - clip_soft)**2 + clip_soft, preds)
+
+        # undo sqrt
+        preds = preds**(1. / track_transform)
+
+      # aggregate over tracks (average)
+      preds = tf.reduce_mean(preds, axis=-1)
+
+      # slice specified positions
+      preds_slice = tf.gather(preds, pos_slice, axis=-1, batch_dims=1)
+      if pos_mask is not None :
+        preds_slice = preds_slice * pos_mask
+
+      # slice denominator positions
+      if use_ratio and pos_slice_denom is not None:
+        preds_slice_denom = tf.gather(preds, pos_slice_denom, axis=-1, batch_dims=1)
+        if pos_mask_denom is not None :
+          preds_slice_denom = preds_slice_denom * pos_mask_denom
+
+      # aggregate over positions
+      if not use_mean :
+        preds_agg = tf.reduce_sum(preds_slice, axis=-1)
+        if use_ratio and pos_slice_denom is not None:
+          preds_agg_denom = tf.reduce_sum(preds_slice_denom, axis=-1)
+      else :
+        if pos_mask is not None :
+          preds_agg = tf.reduce_sum(preds_slice, axis=-1) / tf.reduce_sum(pos_mask, axis=-1)
+        else :
+          preds_agg = tf.reduce_mean(preds_slice, axis=-1)
+
+        if use_ratio and pos_slice_denom is not None:
+          if pos_mask_denom is not None :
+            preds_agg_denom = tf.reduce_sum(preds_slice_denom, axis=-1) / tf.reduce_sum(pos_mask_denom, axis=-1)
+          else :
+            preds_agg_denom = tf.reduce_mean(preds_slice_denom, axis=-1)
+
+      # compute final statistic to take gradient of
+      if no_transform :
+        score_ratios = preds_agg
+      elif not use_ratio :
+        score_ratios = tf.math.log(preds_agg + pseudo_count + 1e-6)
+      else :
+        if not use_logodds :
+          score_ratios = tf.math.log((preds_agg + pseudo_count) / (preds_agg_denom + pseudo_count) + 1e-6)
+        else :
+          score_ratios = tf.math.log(((preds_agg + pseudo_count) / (preds_agg_denom + pseudo_count)) / (1. - ((preds_agg + pseudo_count) / (preds_agg_denom + pseudo_count))) + 1e-6)
+
+    # compute gradient
+    grads = tape.gradient(score_ratios, seq_1hot)
+
+    # zero mean each position
+    if subtract_avg :
+      grads = grads - tf.reduce_mean(grads, axis=-1, keepdims=True)
+
+    # multiply by input 
+    if input_gate :
+      grads = grads * seq_1hot
+
+    return grads
 
-  def gradients(self, seq_1hot, head_i=None, pos_slice=None, batch_size=8, dtype='float16'):
-    """ Compute input gradients sequence. """
+  def gradients_orig(self, seq_1hot, head_i=None, pos_slice=None, batch_size=2, dtype='float16'):
+    """ Compute input gradients sequence (original version of code). """
     # choose model
     if self.ensemble is not None:
       model = self.ensemble
@@ -444,7 +700,7 @@ def gradients(self, seq_1hot, head_i=None, pos_slice=None, batch_size=8, dtype='
       # grads_batch = grads_batch - tf.reduce_mean(grads_batch, axis=-2, keepdims=True)
 
 
-      grads_batch = self.gradients_func(model_batch, seq_1hot, pos_slice)
+      grads_batch = self.gradients_func_orig(model_batch, seq_1hot, pos_slice)
       print('Batch gradient computation in %ds' % (time.time()-t0))
 
       # convert numpy dtype
@@ -460,7 +716,7 @@ def gradients(self, seq_1hot, head_i=None, pos_slice=None, batch_size=8, dtype='
     return grads
 
   @tf.function
-  def gradients_func(self, model, seq_1hot, pos_slice):
+  def gradients_func_orig(self, model, seq_1hot, pos_slice):
     with tf.GradientTape() as tape:
       tape.watch(seq_1hot)