unify xpu and cpu backend and use paged attention

.github/workflows/test_inc.yml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        torch-version: ["2.4.*", "2.5.0"]
+        torch-version: ["2.4.0", "2.5.*"]
 
     runs-on: ubuntu-22.04
 

.github/workflows/test_ipex.yml

@@ -18,8 +18,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        torch-version: ["2.2.0", "2.3.*", "2.4.*"]
-        transformers-version: ["4.39.0", "4.44.*"]
+        transformers-version: ["4.46.*"]
+        torch-version: ["2.4.0", "2.5.0"]
 
     runs-on: ubuntu-22.04
 
@@ -38,10 +38,6 @@ jobs:
           pip install torch==${{ matrix.torch-version }} torchaudio torchvision --extra-index-url https://download.pytorch.org/whl/cpu
           pip install .[ipex,tests] transformers[testing]==${{ matrix.transformers-version }} intel_extension_for_pytorch==${{ matrix.torch-version }}
 
-      - if: ${{ matrix.torch-version == '2.2.0' }}
-        name: Downgrade Numpy
-        run: pip install numpy==1.*
-
       - name: Assert versions
         run: |
           python -c "import torch; print(torch.__version__); assert torch.__version__.startswith('${{ matrix.torch-version }}'.replace('.*', ''))"

optimum/exporters/ipex/cache_utils.py

@@ -0,0 +1,238 @@
+from typing import List, Optional, Tuple
+
+import torch
+from intel_extension_for_pytorch.llm.modules import PagedAttention
+from transformers import Cache, PretrainedConfig
+
+
+class IPEXPagedCache(Cache):
+    """
+    A PagedCache that grows dynamically as more tokens are generated. everytime it grows block-size memory, vendor could set the pageCache memory layout.
+    ipex-xpu:
+    ipex-cpu:
+
+    Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer
+        >>> from optimum.intel import IPEXModelForCausalLM
+        >>> from optimum.exporters.ipex.cache_utils import IPEXPagedCache
+
+        >>> model = IPEXModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", export=True)
+        >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+
+        >>> inputs = tokenizer(text="My name is GPT2", return_tensors="pt")
+
+        >>> # Prepare a cache class and pass it to model's forward
+        >>> past_key_values = IPEXPagedCache()
+        >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
+        >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
+        ```
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        batch_size: int,
+        max_cache_len: int,
+        device,
+        dtype=None,
+        layer_device_map=None,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.batch_size = batch_size
+        # Used in `generate` to keep tally of how many tokens the cache has seen
+        self._seen_tokens = torch.zeros([batch_size], dtype=torch.int32, device=device)
+        self.block_size = 16
+        self.num_blocks = (max_cache_len // self.block_size + (max_cache_len % self.block_size != 0)) * batch_size
+        self.block_tables = -1 * torch.ones([self.num_blocks], dtype=torch.int32, device=device).reshape(
+            batch_size, -1
+        )
+        self.free_blocks = torch.arange(self.num_blocks, device=device)
+        self.max_cache_len = max_cache_len
+        self.num_kv_heads = config.num_key_value_heads
+        self.num_hidden_layers = config.num_hidden_layers
+        if hasattr(config, "head_dim"):
+            head_size = config.head_dim
+        else:
+            head_size = config.hidden_size // config.num_attention_heads
+        self.head_size = head_size
+        self.max_seq_len = 0
+
+        self.key_cache: List[torch.Tensor] = []
+        self.value_cache: List[torch.Tensor] = []
+
+        if device.type == "cpu":
+            key_cache_shape = (self.num_blocks, self.num_kv_heads, self.block_size, head_size)
+            value_cache_shape = (self.num_blocks, self.num_kv_heads, self.block_size, head_size)
+        elif device.type == "xpu":
+            key_cache_shape = (self.num_blocks, self.num_kv_heads, head_size, self.block_size, 1)
+            value_cache_shape = (self.num_blocks, self.num_kv_heads, head_size, self.block_size)
+        for i in range(config.num_hidden_layers):
+            new_layer_key_cache = torch.zeros(key_cache_shape, dtype=dtype, device=device)
+            new_layer_value_cache = torch.zeros(value_cache_shape, dtype=dtype, device=device)
+            self.key_cache.append(new_layer_key_cache)
+            self.value_cache.append(new_layer_value_cache)
+
+    def update_for_prefill(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        batch_size: int,
+        input_lens: torch.Tensor,
+    ):
+        if layer_idx == 0:
+            all_block_indices = []
+            all_slot_offsets = []
+            num_blocks = (input_lens + self.block_size - 1) // self.block_size
+            for i in range(batch_size):
+                for b_idx in range(num_blocks[i]):
+                    if self.block_tables[i][b_idx] == -1:
+                        # need a free block
+                        self.block_tables[i][b_idx] = self.free_blocks[0]
+                        self.free_blocks = self.free_blocks[1:]
+
+                slots_range = torch.arange(input_lens[i], device=key_states.device)
+                block_indices = slots_range // self.block_size
+                slot_offsets = slots_range % self.block_size
+                all_block_indices.append(self.block_tables[i][block_indices])
+                all_slot_offsets.append(slot_offsets)
+
+            all_block_indices = torch.cat(all_block_indices)
+            all_slot_offsets = torch.cat(all_slot_offsets)
+            self.slots = all_block_indices * self.block_size + all_slot_offsets
+
+        # Update the cache
+        PagedAttention.reshape_and_cache(
+            key_states,
+            value_states,
+            self.key_cache[layer_idx],
+            self.value_cache[layer_idx],
+            self.slots,
+        )
+
+        # Update the number of seen tokens
+        if layer_idx == self.num_hidden_layers - 1:
+            self._seen_tokens = self._seen_tokens + input_lens
+            self.max_seq_len, _ = self._seen_tokens.max(dim=0)
+
+    def update_for_decode(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        batch_size: int,
+    ):
+        if layer_idx == 0:
+            start_block_idx = self._seen_tokens // self.block_size
+            num_blocks = (self._seen_tokens + self.block_size) // self.block_size
+            slot_offset_in_block = (self._seen_tokens) % self.block_size
+            self.slots = torch.zeros([batch_size], device=key_states.device, dtype=torch.int32)
+            for i in range(batch_size):
+                for b_idx in range(start_block_idx[i], num_blocks[i]):
+                    if self.block_tables[i][b_idx] == -1:
+                        # need a free block
+                        self.block_tables[i][b_idx] = self.free_blocks[0]
+                        self.free_blocks = self.free_blocks[1:]
+
+                self.slots[i] = self.block_tables[i][start_block_idx[i]] * self.block_size + slot_offset_in_block[i]
+        # Update the cache
+        PagedAttention.reshape_and_cache(
+            key_states,
+            value_states,
+            self.key_cache[layer_idx],
+            self.value_cache[layer_idx],
+            self.slots,
+        )
+
+        # Update the number of seen tokens
+        if layer_idx == self.num_hidden_layers - 1:
+            self._seen_tokens = self._seen_tokens + 1
+            self.max_seq_len = self.max_seq_len + 1
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        attention_mask: torch.Tensor,
+        input_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+        Return:
+            A tuple containing the updated key and value states.
+        """
+
+        batch_size = input_lens.shape[-1]
+        if self.get_seq_length() == 0:
+            # prefill
+            self.update_for_prefill(key_states, value_states, layer_idx, batch_size, input_lens)
+        else:
+            # decode
+            self.update_for_decode(key_states, value_states, layer_idx, batch_size)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states that were seen by the model."""
+        return self.max_seq_len
+
+    def get_max_length(self) -> Optional[int]:
+        """Returns the maximum sequence length of the cached states."""
+        return self.max_cache_len
+
+    def reset(self):
+        """Resets the cache values while preserving the objects"""
+        self._seen_tokens = torch.zeros([self.batch_size], dtype=torch.int32, device=self.block_tables.device)
+        self.block_tables.fill_(-1)
+        self.free_blocks = torch.arange(self.num_blocks, device=self.block_tables.device)
+        self.max_seq_len = 0
+
+    def reorder_cache(self, beam_idx: torch.LongTensor):
+        """Reorders the cache for beam search, given the selected beam indices."""
+        device = self.block_tables.device
+        origin_table = self.block_tables.clone()
+        updated_block_tables = self.block_tables.index_select(0, beam_idx.to(device))
+        mask = self.block_tables.masked_fill(self.block_tables != -1, 1).masked_fill(self.block_tables == -1, 0)
+        num_blocks = mask.cumsum(-1)[:, -1]
+        updated_table = []
+        for i in range(beam_idx.shape[0]):
+            self.block_tables[i, 0 : num_blocks[i] - 1] = updated_block_tables[i, 0 : num_blocks[i] - 1]
+            updated_table.append(self.block_tables[i : i + 1, num_blocks[i] - 1 : num_blocks[i]])
+        updated_table = torch.cat(tuple(updated_table), dim=0)
+        for layer_idx in range(self.num_hidden_layers):
+            self.key_cache[layer_idx][updated_table] = self.key_cache[layer_idx][updated_table[beam_idx]]
+            self.value_cache[layer_idx][updated_table] = self.value_cache[layer_idx][updated_table[beam_idx]]
+        free_table = torch.unique((origin_table[origin_table != self.block_tables]).view(-1))
+        self.free_blocks = torch.cat((self.free_blocks, free_table))
+
+    def crop(self, maximum_length: int):
+        """Crop the past key values up to a new `maximum_length` in terms of tokens. `maximum_length` can also be
+        negative to remove `maximum_length` tokens. This is used in assisted decoding and contrastive search."""
+
+        max_seq_len = self.get_seq_length()
+        if maximum_length < 0:
+            maximum_length = max_seq_len - abs(maximum_length)
+
+        if max_seq_len <= maximum_length:
+            return
+        origin_table = self.block_tables.clone()
+        for bs in range(self._seen_tokens.shape[0]):
+            new_tokens = self._seen_tokens[bs] + maximum_length - max_seq_len
+            num_blocks = (new_tokens + self.block_size - 1) // self.block_size
+            self.block_tables[bs, num_blocks:] = -1
+            self._seen_tokens[bs] = new_tokens
+        self.max_seq_len, _ = self._seen_tokens.max(dim=0)
+        free_table = torch.unique((origin_table[origin_table != self.block_tables]).view(-1))
+        self.free_blocks = torch.cat((self.free_blocks, free_table))

optimum/exporters/ipex/model_patcher.py

@@ -13,11 +13,10 @@
 #  limitations under the License.
 
 from transformers.models.bert.modeling_bert import BertIntermediate
-from transformers.models.falcon.modeling_falcon import FalconDecoderLayer, FalconForCausalLM
-from transformers.models.gpt2.modeling_gpt2 import GPT2Attention, GPT2Block, GPT2LMHeadModel
+from transformers.models.falcon.modeling_falcon import FalconDecoderLayer, FalconModel
+from transformers.models.gpt2.modeling_gpt2 import GPT2Attention, GPT2Model
 from transformers.models.llama.modeling_llama import (
     LlamaDecoderLayer,
-    LlamaForCausalLM,
     LlamaModel,
     LlamaRMSNorm,
 )
@@ -28,7 +27,8 @@
 
 from .modeling_utils import (
     _IPEX_MINIMUM_VERSION_FOR_PATCHING,
-    _gpt2_block_forward,
+    _falcon_model_forward,
+    _gpt2_model_forward,
     _ipex_rms_layer_norm_forward,
     _IPEXFalconDecoderLayer,
     _IPEXGPT2Attention,
@@ -39,8 +39,8 @@
 
 
 # Please also update in the setup.py and .github/workflows/test_ipex.yml if you change the transformers version
-_TRANSFORMERS_MIN_VERSION = "4.39.0"
-_TRANSFORMERS_MAX_VERSION = "4.44.99"
+_TRANSFORMERS_MIN_VERSION = "4.46.0"
+_TRANSFORMERS_MAX_VERSION = "4.46.99"
 
 _IPEX_EXPORTED_GENERATION_TASKS = ("text-generation",)
 
@@ -75,7 +75,7 @@ def patch_op(m, target_m, new_op_name, new_op):
 def _patch_llama_model(model):
     """
     Patch llama model:
-        1. Use IPEX Rope and IAKV cache
+        1. Use IPEX Rope and Paged cache
         2. Linear fusion with (2 Linears + Silu + Mul) and (Linear + Add)
     """
     convert_functions(model, LlamaModel, "forward", _llama_model_forward)
@@ -88,10 +88,14 @@ def _patch_falcon_model(model):
     """
     Patch falcon model:
         1. Disable SDPA so the attention mask will be compatible to ipex attention.
-        2. Use IPEX Rope and IAKV cache
+        2. Use IPEX Rope and paged cache
         3. Linear fusion with (Linear + Gelu) and (Linear + Add + Add)
     """
-    model.transformer._use_sdpa = False
+    num_key_value_heads = (
+        model.config.num_kv_heads if (model.config.new_decoder_architecture or not model.config.multi_query) else 1
+    )
+    setattr(model.config, "num_key_value_heads", num_key_value_heads)
+    convert_functions(model, FalconModel, "forward", _falcon_model_forward)
     replace_customized_linear_with_linear(model)
     convert_class(model, FalconDecoderLayer, _IPEXFalconDecoderLayer, model.config)
     return model
@@ -103,9 +107,10 @@ def _patch_gpt2_model(model):
         1. Disable SDPA so the attention mask will be compatible to ipex attention.
         2. Use IAKV cache
     """
-    model.transformer._attn_implementation = "eager"
+    num_key_value_heads = model.config.num_attention_heads
+    setattr(model.config, "num_key_value_heads", num_key_value_heads)
+    convert_functions(model, GPT2Model, "forward", _gpt2_model_forward)
     convert_class(model, GPT2Attention, _IPEXGPT2Attention, model.config)
-    convert_functions(model, GPT2Block, "forward", _gpt2_block_forward)
     return model
 
 
@@ -136,11 +141,11 @@ def _patch_model(model):
         raise ImportError(
             f"Only transformers versions {_TRANSFORMERS_MIN_VERSION} ~ {_TRANSFORMERS_MAX_VERSION} are verified."
         )
-    if isinstance(model, LlamaForCausalLM):
+    if model.config.model_type == "llama":
         model = _patch_llama_model(model)
-    elif isinstance(model, FalconForCausalLM):
+    elif model.config.model_type == "falcon":
         model = _patch_falcon_model(model)
-    elif isinstance(model, GPT2LMHeadModel):
+    elif model.config.model_type == "gpt2":
         model = _patch_gpt2_model(model)
     elif model.config.model_type == "bert":
         model = _patch_bert_model(model)