Update custom_generate/generate.py

custom_generate/generate.py

@@ -1,18 +1,22 @@
-from typing import Union, Optional, TYPE_CHECKING
+import logging
+from typing import TYPE_CHECKING, Optional, Union
+
 import torch
-from transformers import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
+import torch.nn as nn
+
+from transformers import GenerationConfig, LogitsProcessorList, StoppingCriteriaList
+from transformers.cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from transformers.configuration_utils import PretrainedConfig
 from transformers.generation.utils import (
-    GenerationMixin,
-    GenerateNonBeamOutput,
+    ALL_CACHE_NAMES,
     GenerateDecoderOnlyOutput,
+    GenerateEncoderDecoderOutput,
+    GenerateNonBeamOutput,
+    GenerationMixin,
 )
-from transformers.cache_utils import Cache, EncoderDecoderCache, DynamicCache
 from transformers.modeling_outputs import CausalLMOutputWithPast, Seq2SeqLMOutput
-from transformers.generation.utils import GenerateEncoderDecoderOutput, ALL_CACHE_NAMES
 from transformers.utils import ModelOutput
-from transformers.configuration_utils import PretrainedConfig
-import torch.nn as nn
-import logging
+
 
 if TYPE_CHECKING:
     from transformers.generation.streamers import BaseStreamer
@@ -20,9 +24,7 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 
-def stack_model_outputs(
-    model_outputs: list[ModelOutput], config: PretrainedConfig
-) -> ModelOutput:
+def stack_model_outputs(model_outputs: list[ModelOutput], config: PretrainedConfig) -> ModelOutput:
     """
     Stack a list of ModelOutput objects (or its subclasses) along the batch_size dimension. The function infers the
     specific ModelOutput subclass from the list provided.
@@ -50,17 +52,11 @@ def stack_model_outputs(
             # If the elements of the tuple are also tuples (e.g., past_key_values in our earlier example)
             if isinstance(data[0][0], tuple):
                 return tuple(
-                    tuple(
-                        torch.cat([attr[i][j] for attr in data], dim=0)
-                        for j in range(len(data[0][0]))
-                    )
+                    tuple(torch.cat([attr[i][j] for attr in data], dim=0) for j in range(len(data[0][0])))
                     for i in range(len(data[0]))
                 )
             else:
-                return tuple(
-                    torch.cat([attr[i] for attr in data], dim=0)
-                    for i in range(len(data[0]))
-                )
+                return tuple(torch.cat([attr[i] for attr in data], dim=0) for i in range(len(data[0])))
         elif isinstance(data[0], (int, float)):
             # If the elements are integers or floats, return a tensor
             return torch.tensor(data)
@@ -92,9 +88,7 @@ def _ranking_fast(
     """
     norm_context_hidden = context_hidden / context_hidden.norm(dim=2, keepdim=True)
     norm_next_hidden = next_hidden / next_hidden.norm(dim=2, keepdim=True)
-    cosine_matrix = torch.matmul(
-        norm_context_hidden, norm_next_hidden.transpose(1, 2)
-    ).squeeze(-1)  # [B*K, S]
+    cosine_matrix = torch.matmul(norm_context_hidden, norm_next_hidden.transpose(1, 2)).squeeze(-1)  # [B*K, S]
 
     # Penalize cosine_matrix based on the cosine_matrix_mask (ignore padding positions)
     # Using a large negative value for masked positions
@@ -105,9 +99,7 @@ def _ranking_fast(
     degeneration_penalty, _ = torch.max(cosine_matrix, dim=-1)  # [B*K]
     next_top_k_probs = next_top_k_probs.view(-1)  # [B*K]
     contrastive_score = (1.0 - alpha) * next_top_k_probs - alpha * degeneration_penalty
-    contrastive_score = torch.stack(
-        torch.split(contrastive_score, beam_width)
-    )  # [B, K]
+    contrastive_score = torch.stack(torch.split(contrastive_score, beam_width))  # [B, K]
     _, selected_idx = contrastive_score.max(dim=-1)  # [B]
     return selected_idx
 
@@ -163,9 +155,7 @@ def _contrastive_search(
             f"contrastive search is not supported with stateful models, such as {model.__class__.__name__}"
         )
     # init values
-    has_eos_stopping_criteria = any(
-        hasattr(criteria, "eos_token_id") for criteria in stopping_criteria
-    )
+    has_eos_stopping_criteria = any(hasattr(criteria, "eos_token_id") for criteria in stopping_criteria)
     top_k = generation_config.top_k
     penalty_alpha = generation_config.penalty_alpha
     pad_token_id = generation_config._pad_token_tensor
@@ -181,39 +171,22 @@ def _contrastive_search(
     scores = () if (return_dict_in_generate and output_scores) else None
     decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
     cross_attentions = () if (return_dict_in_generate and output_attentions) else None
-    decoder_hidden_states = (
-        () if (return_dict_in_generate and output_hidden_states) else None
-    )
+    decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
 
     # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
     if return_dict_in_generate and model.config.is_encoder_decoder:
-        encoder_attentions = (
-            model_kwargs["encoder_outputs"].get("attentions")
-            if output_attentions
-            else None
-        )
-        encoder_hidden_states = (
-            model_kwargs["encoder_outputs"].get("hidden_states")
-            if output_hidden_states
-            else None
-        )
+        encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
+        encoder_hidden_states = model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
 
     # keep track of which sequences are already finished
     batch_size, cur_len = input_ids.shape[:2]
-    unfinished_sequences = torch.ones(
-        batch_size, dtype=torch.long, device=input_ids.device
-    )
-    model_kwargs = model._get_initial_cache_position(
-        cur_len, input_ids.device, model_kwargs
-    )
+    unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
+    model_kwargs = model._get_initial_cache_position(cur_len, input_ids.device, model_kwargs)
 
     # Create cosine_matrix_mask based on the attention_mask
     cosine_matrix_mask = torch.ones_like(input_ids, dtype=torch.long)
     if model.config.is_encoder_decoder:
-        if (
-            "decoder_attention_mask" in model_kwargs
-            and model_kwargs["decoder_attention_mask"] is not None
-        ):
+        if "decoder_attention_mask" in model_kwargs and model_kwargs["decoder_attention_mask"] is not None:
             cosine_matrix_mask = model_kwargs["decoder_attention_mask"]
     else:
         cosine_matrix_mask = model_kwargs["attention_mask"]
@@ -221,9 +194,7 @@ def _contrastive_search(
 
     this_peer_finished = False
 
-    while model._has_unfinished_sequences(
-        this_peer_finished, synced_gpus, device=input_ids.device
-    ):
+    while model._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
         # if the first step in the loop, encode all the prefix and obtain: (1) past_key_values;
         # (2) last_hidden_states; (3) logit_for_next_step; (4) update model kwargs for the next step
         if model_kwargs.get("past_key_values") is None or (
@@ -232,9 +203,7 @@ def _contrastive_search(
         ):
             # prepare inputs
             model_kwargs["use_cache"] = True
-            model_inputs = model.prepare_inputs_for_generation(
-                input_ids, **model_kwargs
-            )
+            model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)
 
             # encode the given prefix and prepare model inputs; encoder-decoder model process the prefix and save
             # the `encoder_outputs`
@@ -256,9 +225,7 @@ def _contrastive_search(
             # Copy is needed to avoid keeping a hanging ref to outputs.logits which may be very large for this first iteration
             # (the clone itmodel is always small)
             # torch.float32 is needed to retain precision for later logits manipulations
-            logit_for_next_step = outputs.logits[:, -1, :].to(
-                copy=True, dtype=torch.float32, device=input_ids.device
-            )
+            logit_for_next_step = outputs.logits[:, -1, :].to(copy=True, dtype=torch.float32, device=input_ids.device)
 
             model_kwargs = model._update_model_kwargs_for_generation(
                 outputs,
@@ -282,7 +249,18 @@ def _contrastive_search(
                     f"{model.__class__.__name__} does not support caching and therefore **can't** be used "
                     "for contrastive search."
                 )
-            
+            # Only those caches have the necesary methods
+            elif not (
+                isinstance(past_key_values, DynamicCache)
+                or (
+                    isinstance(past_key_values, EncoderDecoderCache)
+                    and isinstance(past_key_values.self_attention_cache, DynamicCache)
+                )
+            ):
+                raise ValueError(
+                    f"Unsupported cache type: {type(outputs['past_key_values'])}. Contrastive search requires "
+                    "dynamic cache, so set `cache_implementation='dynamic'` in the generation config."
+                )
 
         # contrastive_search main logic start:
         # contrastive search decoding consists of two steps: (1) candidate tokens recall; (2) candidate re-rank by
@@ -300,18 +278,14 @@ def _contrastive_search(
                 scores += (processed_logit_for_next_step,)
             if output_attentions:
                 decoder_attentions += (
-                    (outputs.decoder_attentions,)
-                    if model.config.is_encoder_decoder
-                    else (outputs.attentions,)
+                    (outputs.decoder_attentions,) if model.config.is_encoder_decoder else (outputs.attentions,)
                 )
                 if model.config.is_encoder_decoder:
                     cross_attentions += (outputs.cross_attentions,)
 
             if output_hidden_states:
                 decoder_hidden_states += (
-                    (outputs.decoder_hidden_states,)
-                    if model.config.is_encoder_decoder
-                    else (outputs.hidden_states,)
+                    (outputs.decoder_hidden_states,) if model.config.is_encoder_decoder else (outputs.hidden_states,)
                 )
 
         # This is needed to properly delete outputs.logits which may be very large for this first iteration
@@ -323,8 +297,7 @@ def _contrastive_search(
             past = model_kwargs["past_key_values"]
             # If it is a static cache, modify it in-place layer after layer to save memory
             if isinstance(past, DynamicCache) or (
-                isinstance(past, EncoderDecoderCache)
-                and isinstance(past.self_attention_cache, DynamicCache)
+                isinstance(past, EncoderDecoderCache) and isinstance(past.self_attention_cache, DynamicCache)
             ):
                 past.batch_repeat_interleave(top_k)
             else:
@@ -344,9 +317,7 @@ def _contrastive_search(
             all_outputs = []
             for i in range(top_k):
                 # compute the candidate tokens by the language model and collect their hidden_states
-                next_model_inputs = model.prepare_inputs_for_generation(
-                    top_k_ids[:, i].view(-1, 1), **model_kwargs
-                )
+                next_model_inputs = model.prepare_inputs_for_generation(top_k_ids[:, i].view(-1, 1), **model_kwargs)
 
                 outputs = model(
                     **next_model_inputs,
@@ -356,9 +327,7 @@ def _contrastive_search(
                 )
                 if isinstance(outputs["past_key_values"], DynamicCache) or (
                     isinstance(outputs["past_key_values"], EncoderDecoderCache)
-                    and isinstance(
-                        outputs["past_key_values"].self_attention_cache, DynamicCache
-                    )
+                    and isinstance(outputs["past_key_values"].self_attention_cache, DynamicCache)
                 ):
                     # Remove past K-V from output since we don't need to stack later
                     outputs["past_key_values"] = None
@@ -376,9 +345,7 @@ def _contrastive_search(
         else:
             # compute the candidate tokens by the language model and collect their hidden_states
             # assembles top_k_ids into batch of size k
-            next_model_inputs = model.prepare_inputs_for_generation(
-                top_k_ids.view(-1, 1), **model_kwargs
-            )
+            next_model_inputs = model.prepare_inputs_for_generation(top_k_ids.view(-1, 1), **model_kwargs)
 
             outputs = model(
                 **next_model_inputs,
@@ -424,9 +391,7 @@ def _contrastive_search(
         selected_idx = selected_idx.to("cpu")
 
         # This will be used instead of the previous inneficient torch.stack(torch.split())
-        augmented_idx = torch.tensor(
-            [x + i * top_k for i, x in enumerate(selected_idx)]
-        )
+        augmented_idx = torch.tensor([x + i * top_k for i, x in enumerate(selected_idx)])
 
         # prepare for the next step: (1) next token_id; (2) past_key_values; (3) last_hidden_states for computing
         # the degeneration penalty; (4) logits for selecting next top-k candidates; (5) selected tokens scores
@@ -434,15 +399,11 @@ def _contrastive_search(
         next_tokens = top_k_ids[range(len(top_k_ids)), selected_idx]
         next_hidden = torch.stack(torch.split(next_hidden.squeeze(dim=1), top_k))
         next_hidden = next_hidden[range(batch_size), selected_idx, :]
-        last_hidden_states = torch.cat(
-            [last_hidden_states, next_hidden.unsqueeze(1)], dim=1
-        )
+        last_hidden_states = torch.cat([last_hidden_states, next_hidden.unsqueeze(1)], dim=1)
 
         next_decoder_hidden_states = ()
         for layer in full_hidden_states:
-            layer = torch.stack(torch.split(layer, top_k))[
-                range(batch_size), selected_idx, :
-            ]
+            layer = torch.stack(torch.split(layer, top_k))[range(batch_size), selected_idx, :]
             next_decoder_hidden_states += (layer,)
 
         # generate past_key_values cache of only the selected token
@@ -462,9 +423,7 @@ def _contrastive_search(
         else:
             next_past_key_values = None
             for possible_cache_name in ALL_CACHE_NAMES:
-                next_past_key_values = next_past_key_values or getattr(
-                    outputs, possible_cache_name, None
-                )
+                next_past_key_values = next_past_key_values or getattr(outputs, possible_cache_name, None)
             # Do it in-place layer per layer to save memory
             if isinstance(next_past_key_values, DynamicCache) or (
                 isinstance(next_past_key_values, EncoderDecoderCache)
@@ -482,9 +441,7 @@ def _contrastive_search(
 
                 next_past_key_values = tuple(new_key_values)
 
-        logit_for_next_step = torch.stack(torch.split(logits, top_k))[
-            range(batch_size), selected_idx, :
-        ]
+        logit_for_next_step = torch.stack(torch.split(logits, top_k))[range(batch_size), selected_idx, :]
         logit_for_next_step = logit_for_next_step.to(input_ids.device)
 
         # Rebuilds the relevant parts of the model output for the selected token, for use in the next iteration
@@ -493,14 +450,10 @@ def _contrastive_search(
             next_step_decoder_attentions = ()
             if output_attentions:
                 for layer in outputs.cross_attentions:
-                    layer = torch.stack(torch.split(layer, top_k, dim=0))[
-                        range(batch_size), selected_idx, ...
-                    ]
+                    layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
                     next_step_cross_attentions += (layer,)
                 for layer in outputs.decoder_attentions:
-                    layer = torch.stack(torch.split(layer, top_k, dim=0))[
-                        range(batch_size), selected_idx, ...
-                    ]
+                    layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
                     next_step_decoder_attentions += (layer,)
             outputs = Seq2SeqLMOutput(
                 past_key_values=next_past_key_values,
@@ -512,9 +465,7 @@ def _contrastive_search(
             next_step_attentions = ()
             if output_attentions:
                 for layer in outputs.attentions:
-                    layer = torch.stack(torch.split(layer, top_k, dim=0))[
-                        range(batch_size), selected_idx, ...
-                    ]
+                    layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
                     next_step_attentions += (layer,)
             outputs = CausalLMOutputWithPast(
                 past_key_values=next_past_key_values,
@@ -534,9 +485,7 @@ def _contrastive_search(
 
         # finished sentences should have their next token be a padding token
         if has_eos_stopping_criteria:
-            next_tokens = next_tokens * unfinished_sequences + pad_token_id * (
-                1 - unfinished_sequences
-            )
+            next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
 
         # update generated ids, model inputs, and length for next step
         input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
@@ -544,9 +493,7 @@ def _contrastive_search(
             streamer.put(next_tokens.cpu())
 
         # stop when each sentence is finished
-        unfinished_sequences = unfinished_sequences & ~stopping_criteria(
-            input_ids, scores
-        )
+        unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
         this_peer_finished = unfinished_sequences.max() == 0
 
     if streamer is not None:
@@ -558,9 +505,7 @@ def _contrastive_search(
         if model_kwargs.get("past_key_values") is not None:
             if isinstance(model_kwargs["past_key_values"], DynamicCache) or (
                 isinstance(model_kwargs["past_key_values"], EncoderDecoderCache)
-                and isinstance(
-                    model_kwargs["past_key_values"].self_attention_cache, DynamicCache
-                )
+                and isinstance(model_kwargs["past_key_values"].self_attention_cache, DynamicCache)
             ):
                 model_kwargs["past_key_values"].crop(-1)
             else:
@@ -607,8 +552,7 @@ def generate(model, *args, **kwargs):
     """
     cache_implementation = kwargs.pop("cache_implementation", "dynamic_full")
     if cache_implementation != "dynamic_full" and (
-        "sliding_attention"
-        in getattr(model.config.get_text_config(), "layer_types", [])
+        "sliding_attention" in getattr(model.config.get_text_config(), "layer_types", [])
         or getattr(model.config.get_text_config(), "sliding_window", 0) > 0
     ):
         logger.warning_once(