add basic support for llama3.2 (#12125)

python/llm/src/ipex_llm/transformers/convert.py

@@ -1267,7 +1267,19 @@ def _optimize_post(model, lightweight_bmm=False):
     from ipex_llm.transformers.models.llama import llama_rms_norm_forward
     from ipex_llm.transformers.models.llama import llama_mlp_forward
 
-    if model.config.model_type == "llama":
+    if model.config.model_type == "llama" and model.config.rope_scaling is not None:
+        # llama 3.2
+        modeling_module_name = model.__class__.__module__
+        module = importlib.import_module(modeling_module_name)
+        from ipex_llm.transformers.models.common import rms_norm_forward
+        from ipex_llm.transformers.models.common import mlp_silu_forward
+        from ipex_llm.transformers.models.llama32 import llama_model_forward
+        from ipex_llm.transformers.models.llama32 import llama_attention_forward
+        convert_forward(model, module.LlamaRMSNorm, rms_norm_forward)
+        convert_forward(model, module.LlamaMLP, mlp_silu_forward)
+        convert_forward(model, module.LlamaModel, llama_model_forward)
+        convert_forward(model, module.LlamaAttention, llama_attention_forward)
+    elif model.config.model_type == "llama":
         from transformers.models.llama.modeling_llama import LlamaRMSNorm
         from transformers.models.llama.modeling_llama import LlamaMLP
         from transformers.models.llama.modeling_llama import LlamaAttention

python/llm/src/ipex_llm/transformers/kv.py

@@ -24,12 +24,16 @@ from .models.utils import (
     init_fp8_kv_cache, append_fp8_kv_cache,
     init_kv_cache, append_kv_cache, extend_kv_cache
 )
-from typing import Optional, Dict, Tuple, Any
+from typing import Optional, Dict, Tuple, Any, List
 from transformers.cache_utils import DynamicCache
 from ipex_llm.utils.common.log4Error import invalidInputError
 
 
 class DynamicFp8Cache(DynamicCache):
+    def __init__(self, num_hidden_layers: Optional[int] = None) -> None:
+        # ignore num_hidden_layers to fix transformers >= 4.45
+        super().__init__()
+
     def update(
         self,
         key_states: torch.Tensor,
@@ -37,6 +41,9 @@ class DynamicFp8Cache(DynamicCache):
         layer_idx: int,
         cache_kwargs: Optional[Dict[str, Any]]=None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # fix converting empty DynamicCache in transformers >= 4.45
+        if key_states == []:
+            return key_states, value_states
 
         batch_size, num_heads, seq_len, head_dim = key_states.shape
 
@@ -71,6 +78,10 @@ class DynamicFp8Cache(DynamicCache):
 class DynamicNormalCache(DynamicCache):
     KV_ALLOC_BLOCK_LENGTH = 256
 
+    def __init__(self, num_hidden_layers: Optional[int] = None) -> None:
+        # ignore num_hidden_layers to fix transformers >= 4.45
+        super().__init__()
+
     def update(
         self,
         key_states: torch.Tensor,
@@ -78,6 +89,9 @@ class DynamicNormalCache(DynamicCache):
         layer_idx: int,
         cache_kwargs: Optional[Dict[str, Any]]=None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # fix converting empty DynamicCache in transformers >= 4.45
+        if key_states == []:
+            return key_states, value_states
 
         batch_size, num_heads, seq_len, head_dim = key_states.shape
 
@@ -257,6 +271,9 @@ class DynamicCompressCache(DynamicCache):
         KV_CACHE_ALLOC_BLOCK_LENGTH: int,
         cache_kwargs: Optional[Dict[str, Any]]=None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # fix converting empty DynamicCache in transformers >= 4.45
+        if key_states == []:
+            return key_states, value_states
 
         bsz, num_heads, seq_len, head_dim = key_states.shape
 
@@ -354,6 +371,10 @@ class DynamicCompressFp8Cache(DynamicCompressCache, DynamicFp8Cache):
         KV_CACHE_ALLOC_BLOCK_LENGTH: int,
         cache_kwargs: Optional[Dict[str, Any]]=None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # fix converting empty DynamicCache in transformers >= 4.45
+        if key_states == []:
+            return key_states, value_states
+
         bsz, num_heads, seq_len, head_dim = key_states.shape
 
         if layer_idx == 0:

python/llm/src/ipex_llm/transformers/models/llama32.py

@@ -0,0 +1,236 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Some parts of this file is adapted from
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
+# which is licensed under Apache License 2.0:
+#
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import torch
+
+from typing import Optional, Tuple, Union
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.models.llama.modeling_llama import repeat_kv
+from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
+
+from ipex_llm.utils.common import invalidInputError
+from ipex_llm.transformers.models.common import attention_softmax
+from ipex_llm.transformers.models.utils import use_sdp, use_sdp_causal
+from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
+from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache
+
+
+def llama_model_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Cache] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+) -> Union[Tuple, BaseModelOutputWithPast]:
+    output_attentions = (
+        output_attentions if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+    # IPEX-LLM OPT start: kv cache and quantize kv cache
+    inputs = input_ids if input_ids is not None else inputs_embeds
+    use_cache = True if inputs.device.type == "xpu" else use_cache
+    use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.down_proj, inputs)
+    if use_cache:
+        if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
+            past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
+        elif not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache):
+            past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
+    # IPEX-LLM OPT end
+
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+    invalidInputError((input_ids is None) ^ (inputs_embeds is None),
+                      "You cannot specify both input_ids and inputs_embeds at the same time, "
+                      "and must specify either one")
+
+    if inputs_embeds is None:
+        inputs_embeds = self.embed_tokens(input_ids)
+
+    if cache_position is None:
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        cache_position = torch.arange(
+            past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+        )
+    if position_ids is None:
+        position_ids = cache_position.unsqueeze(0)
+
+    causal_mask = self._update_causal_mask(
+        attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+    )
+    hidden_states = inputs_embeds
+
+    # create position embeddings to be shared across the decoder layers
+    position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+    # decoder layers
+    all_hidden_states = () if output_hidden_states else None
+    all_self_attns = () if output_attentions else None
+    next_decoder_cache = None
+
+    for decoder_layer in self.layers:
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        layer_outputs = decoder_layer(
+            hidden_states,
+            attention_mask=causal_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_values,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+        )
+
+        hidden_states = layer_outputs[0]
+
+        if use_cache:
+            next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+        if output_attentions:
+            all_self_attns += (layer_outputs[1],)
+
+    hidden_states = self.norm(hidden_states)
+
+    # add hidden states from the last decoder layer
+    if output_hidden_states:
+        all_hidden_states += (hidden_states,)
+
+    next_cache = next_decoder_cache if use_cache else None
+
+    if not return_dict:
+        return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                     if v is not None)
+    return BaseModelOutputWithPast(
+        last_hidden_state=hidden_states,
+        past_key_values=next_cache,
+        hidden_states=all_hidden_states,
+        attentions=all_self_attns,
+    )
+
+
+def llama_attention_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Cache] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+    cache_position: Optional[torch.LongTensor] = None,
+    position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
+    **kwargs,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    bsz, q_len, _ = hidden_states.size()
+
+    qkv = self.qkv_proj(hidden_states)
+    qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
+    qkv = qkv.transpose(1, 2)
+    query_states, key_states, value_states = qkv.split([self.num_heads,
+                                                        self.num_key_value_heads,
+                                                        self.num_key_value_heads], dim=1)
+
+    if position_embeddings is None:
+        cos, sin = self.rotary_emb(value_states, position_ids)
+    else:
+        cos, sin = position_embeddings
+    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+    if past_key_value is not None:
+        key_states, value_states = past_key_value.update(key_states, value_states,
+                                                         self.layer_idx, None)
+
+    kv_seq_len = key_states.size(2)
+    if attention_mask is not None:  # no matter the length, we just slice it
+        causal_mask = attention_mask[:, :, :, :kv_seq_len]
+
+    attn_weights = None
+    if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
+        import xe_addons
+        if isinstance(past_key_value, DynamicFp8Cache):
+            attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states, causal_mask)
+        else:
+            attn_output = xe_addons.sdp(query_states, key_states, value_states, causal_mask)
+    elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
+        import xe_addons
+        if isinstance(past_key_value, DynamicFp8Cache):
+            attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
+                                                   value_states, causal_mask)
+        else:
+            attn_output = xe_addons.sdp_causal(query_states, key_states,
+                                               value_states, causal_mask)
+    else:
+        if isinstance(past_key_value, DynamicFp8Cache):
+            key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
+                                                            query_states.dtype)
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states,
+                                    key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if causal_mask is not None:
+            attn_weights = attn_weights + causal_mask
+
+        # upcast attention to fp32
+        attn_weights = attention_softmax(attn_weights, self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.reshape(bsz, q_len, -1)
+    attn_output = self.o_proj(attn_output)
+
+    if not output_attentions:
+        attn_weights = None
+
+    return attn_output, attn_weights, past_key_value