Add torch.compile for Mistral

docs/source/en/llm_optims.md

@@ -29,7 +29,7 @@ To optimize this, you can use a kv-cache to store the past keys and values inste
 The *static kv-cache* solves this issue by pre-allocating the kv-cache size to a maximum value which allows you to combine it with torch.compile for up to a 4x speed up.
 
 > [!WARNING]
-> Currently, only [Command R](./model_doc/cohere), [Gemma](./model_doc/gemma) and [Llama](./model_doc/llama2) models support static kv-cache and torch.compile.
+> Currently, only [Llama](./model_doc/llama2) and a few other models support static kv-cache and torch.compile. Check [this issue](https://github.com/huggingface/transformers/issues/28981) for a live model compatibility list.
 
 For this example, let's load the [Gemma](https://hf.co/google/gemma-2b) model.
 

src/transformers/cache_utils.py

@@ -448,3 +448,124 @@ def reset(self):
             # In-place ops prevent breaking the static address
             self.key_cache[layer_idx].zero_()
             self.value_cache[layer_idx].zero_()
+
+
+class SlidingWindowCache(Cache):
+    """
+    Sliding Window Cache class to be used with `torch.compile` for models like Mistral that support sliding window attention.
+    Every time when we try to update the cache, we compute the `indices` based on `cache_position >= self.config.sliding_window_size - 1`,
+    if true(which means the cache can not hold all the old key value states and new states together because of the sliding window constraint),
+    we need to do a cycle shift based on `indices` to replace the oldest states by the new key value states passed in.
+
+    The `to_shift` is only true once we are above sliding_window_size. Thus with `sliding_window_size==64`:
+
+    indices = (slicing + to_shift[-1].int()-1) % self.config.sliding_window_size
+    tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+        19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
+        37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+        55, 56, 57, 58, 59, 60, 61, 62, 63,  0])
+
+    We overwrite the cache using these, then we always write at cache_position (clamped to `sliding_window_size`)
+
+    Parameters:
+        config (`PretrainedConfig):
+            The configuration file defining the shape-related attributes required to initialize the static cache.
+        max_batch_size (`int`):
+            The maximum batch size with which the model will be used.
+        max_cache_len (`int`):
+            The maximum sequence length with which the model will be used.
+        device (`torch.device`):
+            The device on which the cache should be initialized. Should be the same as the layer.
+        dtype (*optional*, defaults to `torch.float32`):
+            The default `dtype` to use when initializing the layer.
+    """
+
+    def __init__(self, config: PretrainedConfig, max_batch_size: int, max_cache_len: int, device, dtype=None) -> None:
+        if not hasattr(config, "sliding_window") or config.sliding_window is None:
+            raise ValueError(
+                "Setting `cache_implementation` to 'sliding_window' requires the model config supporting "
+                "sliding window attention, please check if there is a `sliding_window` field in the model "
+                "config and it's not set to None."
+            )
+
+        super().__init__()
+        self.max_batch_size = max_batch_size
+        # take the minimum of max_cache_len and config.sliding_window so that we allocate less memory
+        # when we do short-sentence generation
+        self.max_cache_len = config.max_position_embeddings if max_cache_len is None else max_cache_len
+        self.model_sliding_window_size = config.sliding_window
+        self.sliding_window_size = min(self.max_cache_len, self.model_sliding_window_size)
+        # Some model define a custom `head_dim` != config.hidden_size // config.num_attention_heads
+        self.head_dim = (
+            config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads
+        )
+
+        self.dtype = dtype if dtype is not None else torch.float32
+        self.num_key_value_heads = (
+            config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads
+        )
+
+        cache_shape = (
+            config.num_hidden_layers,
+            max_batch_size,
+            self.num_key_value_heads,
+            self.sliding_window_size,
+            self.head_dim,
+        )
+
+        self.key_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
+        self.value_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
+
+        torch._dynamo.mark_static_address(self.key_cache)
+        torch._dynamo.mark_static_address(self.value_cache)
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor]:
+        cache_position = cache_kwargs.get("cache_position")
+        k_out = self.key_cache[layer_idx]
+        v_out = self.value_cache[layer_idx]
+
+        # assume this only happens in prefill phase when prompt length > sliding_window_size
+        if cache_position.shape[0] > self.sliding_window_size:
+            k_out = key_states[:, :, -self.sliding_window_size :, :]
+            v_out = value_states[:, :, -self.sliding_window_size :, :]
+            self.key_cache[layer_idx] = k_out
+            self.value_cache[layer_idx] = v_out
+            # we should return the whole states instead of k_out, v_out to take the whole prompt
+            # into consideration when building kv cache instead of just throwing away tokens outside of the window
+            return key_states, value_states
+
+        slicing = torch.ones(self.sliding_window_size, dtype=torch.long, device=value_states.device).cumsum(0)
+        cache_position = cache_position.clamp(0, self.sliding_window_size - 1)
+        to_shift = cache_position >= self.sliding_window_size - 1
+        indices = (slicing + to_shift[-1].int() - 1) % self.sliding_window_size
+
+        k_out = k_out[:, :, indices]
+        v_out = v_out[:, :, indices]
+
+        k_out[:, :, cache_position] = key_states
+        v_out[:, :, cache_position] = value_states
+
+        self.key_cache[layer_idx] = k_out
+        self.value_cache[layer_idx] = v_out
+
+        return k_out, v_out
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        # assume this will be called only in the first generation step
+        # `cache_postion` will be used in other cases
+        return 0
+
+    def get_max_length(self) -> Optional[int]:
+        # in theory there is no limit because the sliding window size is fixed
+        # no matter how long the sentence is
+        return None
+
+    def reset(self):
+        self.key_cache.zero_()
+        self.value_cache.zero_()

src/transformers/generation/utils.py

@@ -24,7 +24,7 @@
 import torch.distributed as dist
 from torch import nn
 
-from ..cache_utils import Cache, DynamicCache, StaticCache
+from ..cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
 from ..integrations.deepspeed import is_deepspeed_zero3_enabled
 from ..modeling_outputs import CausalLMOutputWithPast, Seq2SeqLMOutput
 from ..models.auto import (
@@ -96,9 +96,7 @@
 if is_accelerate_available():
     from accelerate.hooks import AlignDevicesHook, add_hook_to_module
 
-NEED_SETUP_CACHE_CLASSES_MAPPING = {
-    "static": StaticCache,
-}
+NEED_SETUP_CACHE_CLASSES_MAPPING = {"static": StaticCache, "sliding_window": SlidingWindowCache}
 
 
 @dataclass
@@ -1326,33 +1324,42 @@ def _get_initial_cache_position(self, input_ids, model_kwargs):
         model_kwargs["cache_position"] = torch.arange(past_length, cur_len, device=input_ids.device)
         return model_kwargs
 
-    def _get_static_cache(self, max_batch_size: int, max_cache_len: int) -> StaticCache:
+    def _get_cache(self, cache_implementation: str, max_batch_size: int, max_cache_len: int) -> Cache:
         """
-        Sets a static cache for `generate`, that will persist across calls. A new cache will only be initialized a
+        Sets a cache for `generate`, that will persist across calls. A new cache will only be initialized a
         new `generate` call requires a larger cache.
 
-        Returns the resulting static cache object.
+        Returns the resulting cache object.
         """
-        needs_new_cache = (
-            not hasattr(self, "_static_cache")
-            or self._static_cache.max_batch_size < max_batch_size
-            or self._static_cache.max_cache_len < max_cache_len
+        cache_cls: Cache = NEED_SETUP_CACHE_CLASSES_MAPPING[cache_implementation]
+        need_new_cache = (
+            not hasattr(self, "_cache")
+            or (not isinstance(self._cache, cache_cls))
+            or self._cache.max_batch_size < max_batch_size
         )
-        if needs_new_cache:
+        if cache_implementation == "sliding_window":
+            need_new_cache = need_new_cache or (
+                self._cache.sliding_window_size < self._cache.model_sliding_window_size
+                and max_cache_len > self._cache.max_cache_len
+            )
+        elif cache_implementation == "static":
+            need_new_cache = need_new_cache or self._cache.max_cache_len < max_cache_len
+
+        if need_new_cache:
             if hasattr(self.config, "_pre_quantization_dtype"):
                 cache_dtype = self.config._pre_quantization_dtype
             else:
                 cache_dtype = self.dtype
-            self._static_cache = StaticCache(
+            self._cache = cache_cls(
                 config=self.config,
                 max_batch_size=max_batch_size,
                 max_cache_len=max_cache_len,
                 device=self.device,
                 dtype=cache_dtype,
             )
         else:
-            self._static_cache.reset()  # reset the cache for a new generation
-        return self._static_cache
+            self._cache.reset()
+        return self._cache
 
     def _prepare_special_tokens(
         self,
@@ -1615,14 +1622,14 @@ def generate(
                     "This model does not support the `cache_implementation` argument. Please check the following "
                     "issue: https://github.com/huggingface/transformers/issues/28981."
                 )
-            if generation_config.cache_implementation == "static":
-                if not self._supports_static_cache:
-                    raise ValueError(
-                        "This model does not support `cache_implementation='static'`. Please check the following "
-                        "issue: https://github.com/huggingface/transformers/issues/28981"
-                    )
-                model_kwargs["past_key_values"] = self._get_static_cache(batch_size, generation_config.max_length)
-
+            if generation_config.cache_implementation == "static" and not self._supports_static_cache:
+                raise ValueError(
+                    "This model does not support `cache_implementation='static'`. Please check the following "
+                    "issue: https://github.com/huggingface/transformers/issues/28981"
+                )
+            model_kwargs["past_key_values"] = self._get_cache(
+                generation_config.cache_implementation, batch_size, generation_config.max_length
+            )
         self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
 
         # 7. determine generation mode

src/transformers/models/deprecated/open_llama/modeling_open_llama.py

@@ -63,7 +63,7 @@ def forward(self, hidden_states):
         return self.weight * hidden_states.to(input_dtype)
 
 
-# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->OpenLlama
+# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->OpenLlama
 class OpenLlamaRotaryEmbedding(nn.Module):
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()
@@ -154,7 +154,7 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
+# Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
     """Applies Rotary Position Embedding to the query and key tensors.
 

src/transformers/models/falcon/modeling_falcon.py

@@ -81,7 +81,7 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
+# Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
     """Applies Rotary Position Embedding to the query and key tensors.
 
@@ -123,7 +123,7 @@ def _get_unpad_data(attention_mask):
     )
 
 
-# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Falcon
+# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Falcon
 class FalconRotaryEmbedding(nn.Module):
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()

src/transformers/models/gemma/modeling_gemma.py

@@ -253,7 +253,6 @@ def forward(
         output_attentions: bool = False,
         use_cache: bool = False,
         cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         bsz, q_len, _ = hidden_states.size()
 
@@ -265,8 +264,8 @@ def forward(
         key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
         value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
 
-        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
         if past_key_value is not None:
             # sin and cos are specific to RoPE models; cache_position needed for the static cache

src/transformers/models/gpt_neox/modeling_gpt_neox.py

@@ -522,7 +522,7 @@ def attention_mask_func(attention_scores, ltor_mask):
 
 
 class GPTNeoXRotaryEmbedding(nn.Module):
-    # Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding.__init__
+    # Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding.__init__
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()
 
@@ -614,7 +614,7 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
+# Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
     """Applies Rotary Position Embedding to the query and key tensors.
 

src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py

@@ -230,7 +230,7 @@ def _attn(self, query, key, value, attention_mask=None, head_mask=None):
 
 # Copied from transformers.models.gpt_neox.modeling_gpt_neox.GPTNeoXRotaryEmbedding with GPTNeoXRotaryEmbedding->RotaryEmbedding
 class RotaryEmbedding(nn.Module):
-    # Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding.__init__
+    # Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding.__init__
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()
 

src/transformers/models/idefics/modeling_idefics.py

@@ -478,7 +478,7 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
+# Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
     """Applies Rotary Position Embedding to the query and key tensors.
 

src/transformers/models/llama/modeling_llama.py

@@ -303,7 +303,6 @@ def forward(
         output_attentions: bool = False,
         use_cache: bool = False,
         cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         bsz, q_len, _ = hidden_states.size()
 
@@ -402,7 +401,6 @@ def forward(
         output_attentions: bool = False,
         use_cache: bool = False,
         cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
         if isinstance(past_key_value, StaticCache):
             raise ValueError(