Iterative generation using Input embeds and past_key_values

src/transformers/generation/utils.py

@@ -381,9 +381,13 @@ def prepare_inputs_for_generation(
         # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
         # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
         #              (we can't check exception 3 while compiling)
+        # Excpetion 4: If input_embeds are passed then slice it through `cache_position`, to keep only the unprocessed tokens and
+        # generate the first token for each sequence. Later use the generated Input ids for continuation.
         if past_key_values is not None:
             model_inputs["past_key_values"] = past_key_values
-            if (
+            if inputs_embeds is not None and input_ids.shape[1] == 0:  # Exception 4
+                inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :]
+            elif (
                 inputs_embeds is not None  # Exception 1
                 or (is_torchdynamo_compiling() or cache_position[-1] >= input_ids.shape[1])  # Exception 3
             ):
@@ -393,9 +397,9 @@ def prepare_inputs_for_generation(
 
         # 3. Prepare base model inputs
         input_ids_key = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step for every prompt.
         if not self.config.is_encoder_decoder:
-            if inputs_embeds is not None and cache_position[0] == 0:
+            if inputs_embeds is not None and len(cache_position) == inputs_embeds.shape[1]:
                 model_inputs[input_ids_key] = None
                 model_inputs["inputs_embeds"] = inputs_embeds
             else:

src/transformers/models/bloom/modeling_bloom.py

@@ -895,8 +895,12 @@ def prepare_inputs_for_generation(
         # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
         # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
         #              (we can't check exception 3 while compiling)
+        # Exception 4: If input_embeds are passed then slice it through `cache_position`, to keep only the unprocessed tokens and
+        # generate the first token for each sequence. Later use the generated Input ids for continuation.
         if past_key_values is not None:
-            if (
+            if inputs_embeds is not None and input_ids.shape[1] == 0:  # Exception 4
+                inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :]
+            elif (
                 inputs_embeds is not None  # Exception 1
                 or (is_torchdynamo_compiling() or cache_position[-1] >= input_ids.shape[1])  # Exception 3
             ):
@@ -905,7 +909,7 @@ def prepare_inputs_for_generation(
                 input_ids = input_ids[:, cache_position]
 
         # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and cache_position[0] == 0:
+        if inputs_embeds is not None and len(cache_position) == inputs_embeds.shape[1]:
             model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
         else:
             # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the

src/transformers/models/chameleon/modeling_chameleon.py

@@ -1654,8 +1654,12 @@ def prepare_inputs_for_generation(
         # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
         # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
         #              (we can't check exception 3 while compiling)
+        # Exception 4: If input_embeds are passed then slice it through `cache_position`, to keep only the unprocessed tokens and
+        # generate the first token for each sequence. Later use the generated Input ids for continuation.
         if past_key_values is not None:
-            if (
+            if inputs_embeds is not None and input_ids.shape[1] == 0:  # Exception 4
+                inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :]
+            elif (
                 inputs_embeds is not None  # Exception 1
                 or (is_torchdynamo_compiling() or cache_position[-1] >= input_ids.shape[1])  # Exception 3
             ):
@@ -1668,10 +1672,13 @@ def prepare_inputs_for_generation(
             position_ids = attention_mask.long().cumsum(-1) - 1
             position_ids.masked_fill_(attention_mask == 0, 1)
             if past_key_values:
-                position_ids = position_ids[:, -input_ids.shape[1] :]
+                if inputs_embeds is not None and input_ids.shape[1] == 0:
+                    position_ids = position_ids[:, -inputs_embeds.shape[1] :]
+                else:
+                    position_ids = position_ids[:, -input_ids.shape[1] :]
 
         # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and cache_position[0] == 0:
+        if inputs_embeds is not None and len(cache_position) == inputs_embeds.shape[1]:
             model_inputs = {"inputs_embeds": inputs_embeds}
         else:
             model_inputs = {"input_ids": input_ids.contiguous()}  # `contiguous()` needed for compilation use cases

src/transformers/models/idefics/modeling_idefics.py

@@ -1674,10 +1674,13 @@ def prepare_inputs_for_generation(
         else:
             model_inputs["pixel_values"] = pixel_values
 
-        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+        # If we have cache: let's slice `input_ids` or `input embeds` through `cache_position`, to keep only the unprocessed tokens
         if past_key_values is not None:
             if inputs_embeds is not None:
-                input_ids = input_ids[:, -cache_position.shape[0] :]
+                if input_ids.shape[1] == 0:
+                    inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :]
+                else:
+                    input_ids = input_ids[:, -cache_position.shape[0] :]
             elif input_ids.shape[1] != cache_position.shape[0]:
                 input_ids = input_ids[:, cache_position]
                 if image_attention_mask is not None:
@@ -1687,14 +1690,19 @@ def prepare_inputs_for_generation(
             # create position_ids on the fly for batch generation
             position_ids = attention_mask.long().cumsum(-1) - 1
             position_ids.masked_fill_(attention_mask == 0, 1)
+
+            # If past_key_values are present then slice the postion ids for only only the unprocessed tokens.
             if past_key_values:
-                position_ids = position_ids[:, -input_ids.shape[1] :]
+                if inputs_embeds is not None and input_ids.shape[1] == 0:
+                    position_ids = position_ids[:, -inputs_embeds.shape[1] :]
+                else:
+                    position_ids = position_ids[:, -input_ids.shape[1] :]
 
                 # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
                 position_ids = position_ids.clone(memory_format=torch.contiguous_format)
 
         # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and cache_position[0] == 0:
+        if inputs_embeds is not None and len(cache_position) == inputs_embeds.shape[1]:
             model_inputs.update({"inputs_embeds": inputs_embeds, "input_ids": None})
         else:
             # The clone here is for the same reason as for `position_ids`.

src/transformers/models/moshi/modeling_moshi.py

@@ -1901,8 +1901,7 @@ def forward(
 
 
 @add_start_docstrings(
-    "The original Moshi model with an audio encoder, a Moshi depth decoder and a Moshi decoder, "
-    "for speech-to-speech.",
+    "The original Moshi model with an audio encoder, a Moshi depth decoder and a Moshi decoder, for speech-to-speech.",
     MOSHI_START_DOCSTRING,
 )
 class MoshiForConditionalGeneration(MoshiPreTrainedModel, GenerationMixin):
@@ -2458,18 +2457,57 @@ def prepare_inputs_for_generation(
         blank_user_audio_codes: Optional[torch.FloatTensor] = None,
         **kwargs,
     ):
-        # Overwritten -- Moshi has custom post-processing
-        # 1. Do usual operations done on LLMs like Gemma - because we pre-processed inputs, the first pass always has inputs_embeds
-        model_inputs = super().prepare_inputs_for_generation(
-            input_ids=input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            inputs_embeds=inputs_embeds,
-            cache_position=cache_position,
-            position_ids=position_ids,
-            use_cache=use_cache,
-            logits_to_keep=logits_to_keep,
-            **kwargs,
+        # Overwritten -- Moshi has custom post-processing on the prepared inputs.
+
+        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+        # Exception 1: when passing input_embeds, input_ids may be missing entries
+        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
+        # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
+        # (we can't check exception 3 while compiling)
+
+        if past_key_values is not None:
+            if (
+                inputs_embeds is not None  # Exception 1
+                or (is_torchdynamo_compiling() or cache_position[-1] >= input_ids.shape[1])  # Exception 3
+            ):
+                input_ids = input_ids[:, -cache_position.shape[0] :]
+            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
+                input_ids = input_ids[:, cache_position]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and cache_position[0] == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
+        else:
+            model_inputs = {"input_ids": input_ids, "inputs_embeds": None}
+
+        if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
+            if model_inputs["inputs_embeds"] is not None:
+                batch_size, sequence_length, _ = inputs_embeds.shape
+                device = inputs_embeds.device
+            else:
+                batch_size, sequence_length = input_ids.shape
+                device = input_ids.device
+
+            attention_mask = self.model._prepare_4d_causal_attention_mask_with_cache_position(
+                attention_mask,
+                sequence_length=sequence_length,
+                target_length=past_key_values.get_max_cache_shape(),
+                dtype=self.lm_head.weight.dtype,
+                device=device,
+                cache_position=cache_position,
+                batch_size=batch_size,
+                config=self.config,
+                past_key_values=past_key_values,
+            )
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+            }
         )
 
         # 2. Now that everything is prepared, generate audio_codes using the depth decoder

src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py

@@ -1261,7 +1261,7 @@ def _update_causal_mask(
         if (
             self.config._attn_implementation == "sdpa"
             and attention_mask is not None
-            and attention_mask.device.type == "cuda"
+            and attention_mask.device.type in ["cuda", "xpu"]
             and not output_attentions
         ):
             # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
@@ -1872,8 +1872,12 @@ def prepare_inputs_for_generation(
         # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
         # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
         #              (we can't check exception 3 while compiling)
+        # Exception 4: If input_embeds are passed then slice it through `cache_position`, to keep only the unprocessed tokens and
+        # generate the first token for each sequence. Later use the generated Input ids for continuation.
         if past_key_values is not None:
-            if (
+            if inputs_embeds is not None and input_ids.shape[1] == 0:  # Exception 4
+                inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :]
+            elif (
                 inputs_embeds is not None  # Exception 1
                 or (is_torchdynamo_compiling() or cache_position[-1] >= input_ids.shape[1])  # Exception 3
             ):
@@ -1886,7 +1890,7 @@ def prepare_inputs_for_generation(
             pixel_values_videos = None
 
         # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and cache_position[0] == 0:
+        if inputs_embeds is not None and len(cache_position) == inputs_embeds.shape[1]:
             model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
         else:
             model_inputs = {"input_ids": input_ids, "inputs_embeds": None}

src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py

@@ -770,8 +770,12 @@ def prepare_inputs_for_generation(
         # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
         # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
         #              (we can't check exception 3 while compiling)
+        # Exception 4: If input_embeds are passed then slice it through `cache_position`, to keep only the unprocessed tokens and
+        # generate the first token for each sequence. Later use the generated Input ids for continuation.
         if past_key_values is not None:
-            if (
+            if inputs_embeds is not None and input_ids.shape[1] == 0:  # Exception 4
+                inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :]
+            elif (
                 inputs_embeds is not None  # Exception 1
                 or (is_torchdynamo_compiling() or cache_position[-1] >= input_ids.shape[1])  # Exception 3
             ):
@@ -784,7 +788,7 @@ def prepare_inputs_for_generation(
             pixel_values_videos = None
 
         # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and cache_position[0] == 0:
+        if inputs_embeds is not None and len(cache_position) == inputs_embeds.shape[1]:
             model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
         else:
             model_inputs = {"input_ids": input_ids, "inputs_embeds": None}

src/transformers/models/qwen2_vl/modeling_qwen2_vl.py

@@ -1735,8 +1735,12 @@ def prepare_inputs_for_generation(
         # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
         # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
         #              (we can't check exception 3 while compiling)
+        # Exception 4: If input_embeds are passed then slice it through `cache_position`, to keep only the unprocessed tokens and
+        # generate the first token for each sequence. Later use the generated Input ids for continuation.
         if past_key_values is not None:
-            if (
+            if inputs_embeds is not None and input_ids.shape[1] == 0:  # Exception 4
+                inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :]
+            elif (
                 inputs_embeds is not None  # Exception 1
                 or (is_torchdynamo_compiling() or cache_position[-1] >= input_ids.shape[1])  # Exception 3
             ):
@@ -1749,7 +1753,7 @@ def prepare_inputs_for_generation(
             pixel_values_videos = None
 
         # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and cache_position[0] == 0:
+        if inputs_embeds is not None and len(cache_position) == inputs_embeds.shape[1]:
             model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
         else:
             model_inputs = {"input_ids": input_ids, "inputs_embeds": None}

src/transformers/models/zamba2/modeling_zamba2.py

@@ -1557,7 +1557,7 @@ def _update_causal_mask(self, attention_mask, input_tensor, cache_position):
         if (
             self.config._attn_implementation == "sdpa"
             and attention_mask is not None
-            and attention_mask.device.type == "cuda"
+            and attention_mask.device.type in ["cuda", "xpu"]
         ):
             # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
             # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.