Reworked w/ pre-allocated matrices, verrrrrrrry slow

Author: joecummings

test/torchtext_unittest/prototype/test_generate.py

@@ -90,7 +90,7 @@ def test_hf_DELETE(self) -> None:
             test_sequence_tk,
             max_len=100,
             pad_idx=t5.config.pad_token_id,
-            num_beams=10,
+            num_beams=7,
             beam_size_token=t5.config.vocab_size,
         )
         end = time.time() - start

torchtext/prototype/generate.py

@@ -223,6 +223,12 @@ def beam_search(
             encoder_output_key = "last_hidden_state" if self.is_huggingface_model else "encoder_output"
             encoder_output = model_kwargs["encoder_outputs"][encoder_output_key]
 
+        num_sequences = input_ids.shape[0]
+
+        # Pre-allocate everything
+        token_idxs = torch.full((num_sequences, num_beams, 1), eos_idx).to(dtype=torch.long, device=device)
+        beam_idxs = torch.zeros((num_sequences, num_beams, 1)).to(dtype=torch.long, device=device)
+
         def update_func(emissions, N, T, prev_step_token_idxs, prev_step_hyp_idxs, prev_step_model_states, timestep):
             # `emissions` and `N` are unused in this current implementation
 
@@ -231,16 +237,8 @@ def update_func(emissions, N, T, prev_step_token_idxs, prev_step_hyp_idxs, prev_
             # For first timestep, create previous step token_idxs and model_states
             if timestep == 0:
                 prev_step_token_idxs = [-1]
-                prev_step_model_states = [
-                    create_emitting_model_state(
-                        Seq2SeqModelState(timestep=0, sequence=input_ids[i].unsqueeze(0), lm_scores=None)
-                    )
-                ]
 
             encoder_output_for_curr_seq = encoder_output[i, :, :].unsqueeze(0) if self.is_encoder_decoder else None
-            prev_model_state_sequences = [
-                get_obj_from_emitting_model_state(state).sequence for state in prev_step_model_states
-            ]
             out_probs, model_states = [], []
 
             start = 0
@@ -256,66 +254,32 @@ def update_func(emissions, N, T, prev_step_token_idxs, prev_step_hyp_idxs, prev_
                 if end > curr_beam_size:
                     end = curr_beam_size
 
-                num_samples = end - start
-
                 if prev_step_token_idxs != [-1]:
-                    state_sequences = torch.cat(prev_model_state_sequences[start:end], dim=0)
-                    token_indices = (
-                        torch.Tensor(prev_step_token_idxs[start:end])
-                        .to(dtype=torch.long, device=device)
-                        .reshape(num_samples, 1)
-                    )
-
-                    state_and_tokens = torch.cat(
-                        [state_sequences, token_indices], dim=-1
-                    )  # [batch_size x (timestep + 1)]
-                    assert state_and_tokens.shape == (
-                        num_samples,
-                        timestep + 1,
-                    ), f"state_and_tokens has shape {state_and_tokens.shape} = expected {(num_samples, timestep + 1)}"
+                    token_indices = torch.Tensor(prev_step_token_idxs[start:end]).to(dtype=torch.long, device=device)
+                    token_idxs[i, : len(token_indices), 0] = token_indices
+                    curr_token_idxs = token_idxs[i, :, 0].reshape(num_beams, 1)
                 else:
-                    assert len(prev_model_state_sequences) == 1
-                    state_and_tokens = token_indices = prev_model_state_sequences[0].expand(
-                        num_beams, -1
-                    )  # TODO: Make this more robust
-
-                # Cleanup -- combine this with the above
-                if self.is_encoder_decoder:
-                    # Expand encoder outputs along the batch dimension so that they match the decoder input state's batch size
-                    # This is a view-only operation and doesn't copy
-                    model_kwargs["encoder_outputs"][encoder_output_key] = encoder_output_for_curr_seq.expand(
-                        num_samples if timestep > 0 else num_beams, -1, -1
-                    )
+                    if self.is_encoder_decoder:
+                        # Expand encoder outputs along the batch dimension so that they match the decoder input state's batch size
+                        # This is a view-only operation and doesn't copy
+                        model_kwargs["encoder_outputs"][encoder_output_key] = encoder_output_for_curr_seq.expand(
+                            num_beams, -1, -1
+                        )
+                    curr_token_idxs = torch.zeros((num_beams, 1)).to(dtype=torch.long, device=device)
+                
 
                 # Preprocess inputs for generation
                 model_inputs = self.model.prepare_inputs_for_generation(
-                    token_indices, **model_kwargs
+                    curr_token_idxs, **model_kwargs
                 )  # This should technically work with state_and_tokens, but the prepare function has to splice if past (like HF does)
                 if self.is_huggingface_model:
                     model_inputs.update(self._huggingface_model_input_values)
                     if len(prev_step_hyp_idxs) > 1 and model_kwargs["past"] is not None:
-                        beam_idxs = torch.Tensor(prev_step_hyp_idxs).to(dtype=torch.int32)
-
-                        # We could store this in model_kwargs
-                        num_hyps_in_prev_step = model_kwargs["past"][0][0].shape[0]
-
-                        num_finished_hyps_in_step = num_hyps_in_prev_step - len(prev_step_hyp_idxs)
-                        if num_finished_hyps_in_step > 0:
-                            beam_idxs = F.pad(beam_idxs, (0, num_finished_hyps_in_step), "constant", 0)
-
-                        beam_idxs = torch.clamp(beam_idxs, max=len(prev_step_hyp_idxs) - 1)
-
-                        reordered_cached = self.model._reorder_cache(model_kwargs["past"], beam_idxs)
-
-                        if num_finished_hyps_in_step > 0:
-                            sliced_cache = ()
-                            for states in reordered_cached:
-                                sliced_state = ()
-                                for state in states:
-                                    sliced_state = sliced_state + (state[: len(prev_step_hyp_idxs)],)
-                                sliced_cache = sliced_cache + (sliced_state,)
-                            reordered_cached = sliced_cache
+                        beam_indices = torch.Tensor(prev_step_hyp_idxs).to(dtype=torch.int32)
+                        beam_idxs[i, : len(prev_step_hyp_idxs), 0] = beam_indices
+                        curr_beam_idxs = beam_idxs[i, :, 0]
 
+                        reordered_cached = self.model._reorder_cache(model_kwargs["past"], curr_beam_idxs)
                         model_inputs["past_key_values"] = reordered_cached
 
                 # Forward pass
@@ -329,18 +293,21 @@ def update_func(emissions, N, T, prev_step_token_idxs, prev_step_hyp_idxs, prev_
                 if self.is_huggingface_model:
                     self._update_model_kwargs_for_generation(outputs, model_kwargs)
 
+                # Reset
+                token_idxs[i, :, 0] = eos_idx
+                beam_idxs[i, :, 0] = 0
+
                 # Keep track of probabilities over vocab for this pairing
-                # TODO: fix how we track the number here?
-                for i in range(lm_scores.shape[0]):
+                for i in range(num_beams):
                     sample_lm_scores = lm_scores[i, -1]
                     out_probs.append(sample_lm_scores.tolist())
                     # Keep track of sequence and decoder hidden states
                     model_states.append(
                         create_emitting_model_state(
                             Seq2SeqModelState(
                                 timestep=timestep,
-                                sequence=state_and_tokens[i].unsqueeze(0),
-                                lm_scores=sample_lm_scores,
+                                sequence=[],
+                                lm_scores=0,
                             )
                         )
                     )
@@ -386,10 +353,6 @@ def is_not_neg_one(elem: int) -> bool:
             if not self.is_encoder_decoder:
                 final_tokens = input_ids[timestep].tolist() + final_tokens
 
-            # Makeshift padding so that we can stack the tensors
-            while len(final_tokens) < max_len:
-                final_tokens += [0]
-
             # Convert from list to tensors
             final_tokens_as_tensors = torch.Tensor(final_tokens).to(torch.long)