refactor device check and remove cohere/mixtral support (#12659)

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

@@ -1710,31 +1710,6 @@ def _optimize_post(model):
         convert_forward(model, module.VisionAttention, qwen2_vision_attention_forward)
         convert_forward(model, module.Qwen2VLModel, qwen2_vl_model_forward)
         convert_forward(model, module.Qwen2VLAttention, qwen2_vl_attention_forward)
-    elif model.config.model_type == "cohere":
-        # for CohereForAI/c4ai-command-r-v01
-        invalidInputError(version.parse(trans_version) >= version.parse("4.40.0"),
-                          "Please upgrade transformers to 4.40.0 or higher version "
-                          "to run Mixtral models.")
-        modeling_module_name = model.__class__.__module__
-        module = importlib.import_module(modeling_module_name)
-        if version.parse(trans_version) >= version.parse("4.41.0"):
-            from ipex_llm.transformers.models.cohere import cohere_model_forward_4_41
-            convert_forward(model,
-                            module.CohereModel,
-                            cohere_model_forward_4_41)
-        else:
-            from ipex_llm.transformers.models.cohere import cohere_model_forward
-            convert_forward(model,
-                            module.CohereModel,
-                            cohere_model_forward)
-
-        from ipex_llm.transformers.models.cohere import cohere_attention_forward
-        convert_forward(model,
-                        module.CohereAttention,
-                        cohere_attention_forward)
-        convert_forward(model,
-                        module.CohereMLP,
-                        mlp_silu_forward)
     elif model.config.model_type == "aquila":
         modeling_module_name = model.__class__.__module__
         module = importlib.import_module(modeling_module_name)
@@ -1746,31 +1721,6 @@ def _optimize_post(model):
         convert_forward(model,
                         module.AquilaRMSNorm,
                         rms_norm_forward)
-    elif model.config.model_type == "mixtral":
-        # For mistralai/Mixtral-8x7B-v0.1
-        invalidInputError(version.parse(trans_version) >= version.parse("4.36.0"),
-                          "Please upgrade transformers to 4.36.0 or higher version "
-                          "to run Mixtral models.")
-        modeling_module_name = model.__class__.__module__
-        module = importlib.import_module(modeling_module_name)
-        from ipex_llm.transformers.models.mixtral import mixtral_moeblock_forward, \
-            mixtral_attention_forward, mixtral_mlp_forward, mixtral_model_forward
-        convert_forward(model,
-                        module.MixtralAttention,
-                        mixtral_attention_forward)
-        convert_forward(model,
-                        module.MixtralRMSNorm,
-                        rms_norm_forward)
-        convert_forward(model,
-                        module.MixtralSparseMoeBlock,
-                        mixtral_moeblock_forward)
-        convert_forward(model,
-                        module.MixtralBLockSparseTop2MLP,
-                        mixtral_mlp_forward)
-        convert_forward(model,
-                        module.MixtralModel,
-                        mixtral_model_forward)
-
     elif model.config.model_type == "phi-msft" and \
             hasattr(model.config, "num_local_experts"):
         # For phixtral, limit the condition to avoid applying on phi-2 hosted by ModelScope
@@ -1785,16 +1735,6 @@ def _optimize_post(model):
                         module.MLP,
                         phixtral_mlp_forward)
     elif model.config.model_type == "mistral":
-        if model.config.architectures is not None and \
-                model.config.architectures[0] == "MixtralForCausalLM":
-            # For DiscoResearch/mixtral-7b-8expert
-            invalidInputError(version.parse(trans_version) >= version.parse("4.36.0"),
-                              "Please upgrade transformers to 4.36.0 or higher version "
-                              "to run Mixtral models.")
-            modeling_module_name = model.__class__.__module__
-            module = importlib.import_module(modeling_module_name)
-            convert_forward(model, module.MistralRMSNorm, rms_norm_forward)
-        else:
         modeling_module_name = model.__class__.__module__
         module = importlib.import_module(modeling_module_name)
 

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

@@ -33,7 +33,7 @@ from ipex_llm.transformers.speculative import greedy, deepmind_sample, logits_to
     _crop_past_key_values, _prepare_generate_args, _non_cpu_ipex_verify, clear_benchmarks,\
     _prepare_generate_args_4_45
 from ipex_llm.utils.common import invalidInputError
-from ipex_llm.transformers.utils import get_xpu_device_type
+from ipex_llm.transformers.utils import get_xpu_device_name
 
 logger = logging.getLogger("ipex_llm.lookup")
 
@@ -295,7 +295,7 @@ def lookup_generate(self,
     invalidInputError(input_ids.shape[0] == 1,
                       "Prompt lookup is currently not supported with batch inference.")
 
-    device_name = get_xpu_device_type(input_ids)
+    device_name = get_xpu_device_name(input_ids.device)
 
     candidates_generator = PromptLookupCandidateGenerator(
         num_output_tokens=num_output_tokens,

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

@@ -51,7 +51,7 @@ from torch import Tensor, device, dtype, nn
 from operator import mul
 from functools import reduce
 from ipex_llm.transformers.xpu_customize_fwd import custom_fwd, custom_bwd
-from ipex_llm.transformers.utils import get_autocast_dtype, get_xpu_device_type, \
+from ipex_llm.transformers.utils import get_autocast_dtype, get_xpu_device_name, \
     get_ipex_version
 from ipex_llm.transformers.convert import is_deepspeed_available, get_use_vllm
 
@@ -266,7 +266,7 @@ def reshape_lm_head_input(x):
 
 
 def use_batch_forward(x: torch.Tensor, qtype: int, output_len: int):
-    device = get_xpu_device_type(x)
+    device_name = get_xpu_device_name(x.device)
     batch_size = x.shape[0]
     hard_condition = (
         x.dtype in [torch.float, torch.half]
@@ -286,7 +286,7 @@ def use_batch_forward(x: torch.Tensor, qtype: int, output_len: int):
             or (
                 qtype in [SYM_INT8, FP4, FP6, Q4_K, Q6_K]
                 and batch_size <= 48
-                and device in ["arc", "flex", "pvc", "mtl"]
+                and device_name in ["arc", "pvc", "mtl", "lnl", "arl"]
                 and x.shape[1] % 256 == 0
                 and output_len % 32 == 0
             )
@@ -295,8 +295,8 @@ def use_batch_forward(x: torch.Tensor, qtype: int, output_len: int):
     if hard_condition:
         return (
             batch_size > 1
-            or (device in ["arc", "flex"] and qtype in [SYM_INT8, FP4])
+            or (device in ["arc"] and qtype in [SYM_INT8, FP4])
-            or (device in ["arc", "flex", "mtl"] and qtype in [FP8E4])
+            or (device in ["arc", "mtl"] and qtype in [FP8E4])
             or (device in ["lnl"] and qtype in [SYM_INT4] and x.shape[1] % 512 == 0)
             or (device in ["bmg"] and qtype in [SYM_INT4, FP8E5])
         )
@@ -603,7 +603,7 @@ class LowBitLinear(nn.Linear):
         # empty cache before and after lm_head at first token when input > 1024
         # on arc or IPEX_LLM_LOW_MEM is set to 1 at inference time.
         if self.device is None:
-            self.device = get_xpu_device_type(self.weight.data)
+            self.device = get_xpu_device_name(self.weight.data.device)
             self.low_memory_mode = \
                 self.low_memory_mode and \
                 (self.device == "arc" or os.environ.get("IPEX_LLM_LOW_MEM", None) == "1")
@@ -782,7 +782,7 @@ class FP16Linear(nn.Linear):
         if not self.use_esimd_kernel(x):
             if (
                 get_ipex_version() < "2.1.10+xpu"
-                or get_xpu_device_type(x) not in ["arc", "flex", "pvc"]
+                or get_xpu_device_name(x.device) not in ["arc", "pvc"]
                 or self.disable_fp16_opt
             ):
                 if self.weight_type == 2:
@@ -848,7 +848,7 @@ class FP16Linear(nn.Linear):
             return result.to(x.dtype)
 
     def use_esimd_kernel(self, x):
-        gpu_type = get_xpu_device_type(x)
+        gpu_type = get_xpu_device_name(x.device)
         if self.disable_fp16_opt:
             return False
         # esimd kernel can only be used for Arc and Flex

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

@@ -1,589 +0,0 @@
-#
-# 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/cohere/modeling_cohere.py
-
-# coding=utf-8
-# Copyright 2024 Cohere 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.
-
-# This file is based on the LLama model definition file in transformers
-
-"""PyTorch Cohere model."""
-import math
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-import torch.utils.checkpoint
-from typing import Optional, Tuple, List
-from ipex_llm.transformers.models.utils import repeat_kv
-from ipex_llm.transformers.models.utils import extend_kv_cache, append_kv_cache
-from transformers.models.cohere.modeling_cohere import apply_rotary_pos_emb
-from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
-from ipex_llm.transformers.models.utils import use_decoding_fast_path
-from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp
-from transformers.models.cohere.modeling_cohere import apply_rotary_pos_emb
-from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
-from ipex_llm.transformers.kv import DynamicFp8Cache
-from ipex_llm.transformers.models.utils import should_use_fuse_rope
-from transformers.modeling_outputs import BaseModelOutputWithPast
-from ipex_llm.utils.common import invalidInputError
-try:
-    from transformers.cache_utils import Cache, DynamicCache
-except ImportError:
-    Cache = Tuple[torch.Tensor]
-
-KV_CACHE_ALLOC_BLOCK_LENGTH = 256
-
-
-def cohere_model_forward(
-    self,
-    input_ids: torch.LongTensor = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[List[torch.FloatTensor]] = 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,
-):
-    use_cache = use_cache if use_cache is not None \
-        else self.config.use_cache
-    if use_cache and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input_ids):
-        if not isinstance(past_key_values, DynamicFp8Cache):
-            past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
-    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
-    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-    if input_ids is not None and inputs_embeds is not None:
-        invalidInputError(False,
-                          "You cannot specify both input_ids and inputs_embeds at the same time")
-
-    if self.gradient_checkpointing and self.training and use_cache:
-        invalidInputError(False,
-                          "`use_cache=True` is incompatible "
-                          "with gradient checkpointing. Setting `use_cache=False`.")
-        use_cache = False
-
-    if inputs_embeds is None:
-        inputs_embeds = self.embed_tokens(input_ids)
-
-    past_seen_tokens = 0
-    if use_cache:  # kept for BC (cache positions)
-        if not isinstance(past_key_values, Cache):
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-        past_seen_tokens = past_key_values.get_seq_length()
-
-    if cache_position is None:
-        if isinstance(past_key_values, Cache):
-            invalidInputError(False, "cache_position is a required argument when using Cache.")
-        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_seen_tokens)
-
-    # embed positions
-    hidden_states = inputs_embeds
-
-    # 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,)
-
-        if self.gradient_checkpointing and self.training:
-            layer_outputs = self._gradient_checkpointing_func(
-                decoder_layer.__call__,
-                hidden_states,
-                causal_mask,
-                position_ids,
-                past_key_values,
-                output_attentions,
-                use_cache,
-                cache_position,
-            )
-        else:
-            # ipex-llm changes
-            curr_device = decoder_layer.input_layernorm.weight.device
-            if causal_mask is not None:
-                causal_mask = causal_mask.to(curr_device)
-            if position_ids is not None:
-                position_ids = position_ids.to(curr_device)
-            # ipex-llm changes end
-            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,
-            )
-
-        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 cohere_model_forward_4_41(
-    self,
-    input_ids: torch.LongTensor = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[List[torch.FloatTensor]] = 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,
-):
-    use_cache = use_cache if use_cache is not None \
-        else self.config.use_cache
-    if use_cache and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input_ids):
-        if not isinstance(past_key_values, DynamicFp8Cache):
-            past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
-    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
-    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-    if input_ids is not None and inputs_embeds is not None:
-        invalidInputError(False,
-                          "You cannot specify both input_ids and inputs_embeds at the same time")
-
-    if self.gradient_checkpointing and self.training and use_cache:
-        invalidInputError(False,
-                          "`use_cache=True` is incompatible "
-                          "with gradient checkpointing. Setting `use_cache=False`.")
-        use_cache = False
-
-    if inputs_embeds is None:
-        inputs_embeds = self.embed_tokens(input_ids)
-
-    past_seen_tokens = 0
-    return_legacy_cache = False
-    # kept for BC (non `Cache` `past_key_values` inputs)
-    if use_cache and not isinstance(past_key_values, Cache):
-        return_legacy_cache = True
-        past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-
-    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
-    )
-
-    # embed positions
-    hidden_states = inputs_embeds
-
-    # 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,)
-
-        if self.gradient_checkpointing and self.training:
-            layer_outputs = self._gradient_checkpointing_func(
-                decoder_layer.__call__,
-                hidden_states,
-                causal_mask,
-                position_ids,
-                past_key_values,
-                output_attentions,
-                use_cache,
-                cache_position,
-            )
-        else:
-            # ipex-llm changes
-            curr_device = decoder_layer.input_layernorm.weight.device
-            if causal_mask is not None:
-                causal_mask = causal_mask.to(curr_device)
-            if position_ids is not None:
-                position_ids = position_ids.to(curr_device)
-            # ipex-llm changes end
-            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,
-            )
-
-        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 return_legacy_cache:
-        next_cache = next_cache.to_legacy_cache()
-    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 cohere_attention_forward(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    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 use_quantize_kv_cache(self.q_proj, hidden_states):
-        forward_function = cohere_attention_forward_quantized
-    else:
-        forward_function = cohere_attention_forward_origin
-    return forward_function(
-        self=self,
-        hidden_states=hidden_states,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_value=past_key_value,
-        output_attentions=output_attentions,
-        use_cache=use_cache,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-
-def cohere_attention_forward_quantized(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    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()
-
-    query_states = self.q_proj(hidden_states)
-    key_states = self.k_proj(hidden_states)
-    value_states = self.v_proj(hidden_states)
-
-    query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
-    key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
-    if self.use_qk_norm:
-        query_states = self.q_norm(query_states)
-        key_states = self.k_norm(key_states)
-
-    query_states = query_states.transpose(1, 2)
-    key_states = key_states.transpose(1, 2)
-    value_states = value_states.view(bsz, q_len,
-                                     self.num_key_value_heads, self.head_dim).transpose(1, 2)
-
-    past_key_value = getattr(self, "past_key_value", past_key_value)
-    kv_seq_len = key_states.shape[-2]
-    if past_key_value is not None:
-        kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-    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; position_ids needed for the static cache
-        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-        key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx,
-                                                         cache_kwargs, new_layout=True)
-    if q_len == 1 and query_states.device.type == 'xpu' and not self.training \
-            and not hidden_states.requires_grad:
-        import xe_addons
-        attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states, attention_mask)
-        attn_weights = None
-    else:
-        key_states, value_states = restore_fp8_kv_cache(key_states,
-                                                        value_states, query_states.dtype)
-        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 attention_mask is not None:  # no matter the length, we just slice it
-            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-            attn_weights = attn_weights + causal_mask
-
-        # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights,
-                                             dim=-1, dtype=torch.float32).to(query_states.dtype)
-        attn_weights = nn.functional.dropout(attn_weights,
-                                             p=self.attention_dropout, training=self.training)
-        attn_output = torch.matmul(attn_weights, value_states)
-
-    invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
-                      "`attn_output` should be of size "
-                      f"{(bsz, self.num_heads, q_len, self.head_dim)},"
-                      f" but is {attn_output.size()}")
-
-    attn_output = attn_output.transpose(1, 2).contiguous()
-
-    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-    attn_output = self.o_proj(attn_output)
-
-    if not output_attentions:
-        attn_weights = None
-
-    return attn_output, attn_weights, past_key_value
-
-
-def cohere_attention_forward_origin(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: bool = False,
-    use_cache: bool = False,
-    cache_position: Optional[torch.LongTensor] = None,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    bsz, q_len, _ = hidden_states.size()
-    device = hidden_states.device
-    use_fuse_rope = should_use_fuse_rope(hidden_states, position_ids, self.training)
-    enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
-    decoding_fast_path = use_decoding_fast_path(self.q_proj,
-                                                use_fuse_rope,
-                                                enough_kv_room,
-                                                bsz * q_len)
-    if decoding_fast_path:
-        hidden_states = hidden_states.view(1, -1)
-        cache_k = past_key_value.key_cache[self.layer_idx]
-        cache_v = past_key_value.value_cache[self.layer_idx]
-        kv_seq_len = cache_k.shape[-2]
-        import xe_linear
-        query_states, key_states, value_states = xe_linear.forward_qkv(hidden_states,
-                                                                       self.q_proj.weight,
-                                                                       self.k_proj.weight,
-                                                                       self.v_proj.weight,
-                                                                       position_ids,
-                                                                       cache_k, cache_v,
-                                                                       self.q_proj.weight.qtype,
-                                                                       self.v_proj.weight.qtype,
-                                                                       kv_seq_len,
-                                                                       self.head_dim,
-                                                                       self.rotary_emb.base,)
-        kv_seq_len += 1
-        # update past_key_value's seem_tokens and kv caches.
-        if self.layer_idx == 0:
-            past_key_value._seen_tokens = kv_seq_len
-        past_key_value.key_cache[self.layer_idx] = key_states
-        past_key_value.value_cache[self.layer_idx] = value_states
-    else:
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
-        if self.use_qk_norm:
-            query_states = self.q_norm(query_states)
-            key_states = self.k_norm(key_states)
-
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
-                                         self.head_dim).transpose(1, 2)
-
-        past_key_value = getattr(self, "past_key_value", past_key_value)
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            if self.layer_idx is None:
-                invalidInputError(
-                    False,
-                    "The cache structure has changed since version v4.36. "
-                    f"If you are using {self.__class__.__name__} "
-                    "for auto-regressive decoding with k/v caching, "
-                    "please make sure to initialize the attention class with a layer index."
-                )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-        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:
-            if self.layer_idx == 0:
-                past_key_value._seen_tokens += key_states.shape[-2]
-
-            if len(past_key_value.key_cache) <= self.layer_idx:
-                past_key_value.key_cache.append(key_states)
-                past_key_value.value_cache.append(value_states)
-            else:
-                cache_k = past_key_value.key_cache[self.layer_idx]
-                cache_v = past_key_value.value_cache[self.layer_idx]
-
-                if not enough_kv_room:
-                    # allocate new
-                    new_c_k, new_c_v = extend_kv_cache(bsz,
-                                                       self.num_key_value_heads,  # Support GQA
-                                                       self.head_dim,
-                                                       cache_k.size(2),
-                                                       kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
-                                                       dtype=cache_k.dtype,
-                                                       device=device)
-
-                    new_c_k[:] = cache_k
-                    new_c_v[:] = cache_v
-                    cache_k = new_c_k
-                    cache_v = new_c_v
-
-                key_states, value_states = append_kv_cache(cache_k,
-                                                           cache_v,
-                                                           key_states,
-                                                           value_states)
-
-                # update past_key_value
-                past_key_value.key_cache[self.layer_idx] = key_states
-                past_key_value.value_cache[self.layer_idx] = value_states
-
-    key_states = repeat_kv(key_states, self.num_key_value_groups)
-    value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-    if not self.training and not hidden_states.requires_grad and \
-            use_flash_attention(query_states, key_states, attention_mask):
-        attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
-                                                     key_states.to(device, dtype=torch.float16),
-                                                     value_states.to(device, dtype=torch.float16),
-                                                     is_causal=True)
-        attn_weights = None
-    elif not self.training and not hidden_states.requires_grad and \
-            use_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
-        import xe_addons
-        if attention_mask is not None:
-            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-        else:
-            causal_mask = None
-        attn_output = xe_addons.sdp(query_states, key_states, value_states, causal_mask)
-        attn_output = attn_output.view(query_states.shape)
-        attn_weights = None
-    else:
-        attn_weights = torch.matmul(query_states,
-                                    key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-        if attention_mask is not None:  # no matter the length, we just slice it
-            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-            attn_weights = attn_weights + causal_mask
-
-        # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights,
-                                             dim=-1, dtype=torch.float32).to(query_states.dtype)
-        attn_weights = nn.functional.dropout(attn_weights,
-                                             p=self.attention_dropout, training=self.training)
-        attn_output = torch.matmul(attn_weights, value_states)
-
-    invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
-                      "`attn_output` should be of size "
-                      f"{(bsz, self.num_heads, q_len, self.head_dim)},"
-                      f" but is {attn_output.size()}")
-
-    attn_output = attn_output.transpose(1, 2).contiguous()
-
-    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-    attn_output = self.o_proj(attn_output)
-
-    if not output_attentions:
-        attn_weights = None
-
-    return attn_output.to(hidden_states.dtype), attn_weights, past_key_value

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

@@ -53,10 +53,10 @@ def siglip_attention_forward(
     qkv = qkv.transpose(1, 2)
     query_states, key_states, value_states = qkv.chunk(3, dim=1)
 
-    from ipex_llm.transformers.utils import get_xpu_device_type
+    from ipex_llm.transformers.utils import get_xpu_device_name
     if (
         self.head_dim == 72
-        and get_xpu_device_type(query_states) in ["arc", "flex"] and
+        and get_xpu_device_name(query_states.device) == "arc" and
         query_states.dtype in [torch.float, torch.half]
     ):
         n_heads, kv_length = query_states.size(1), key_states.size(2)

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

@@ -1,576 +0,0 @@
-#
-# 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/mixtral/modeling_mixtral.py
-
-# coding=utf-8
-# Copyright 2023 Mistral AI 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.
-
-""" PyTorch Mixtral model."""
-import math
-from typing import Optional, Tuple, Union, List
-from transformers.modeling_outputs import MoeModelOutputWithPast
-from transformers.cache_utils import Cache, DynamicCache
-from transformers.modeling_attn_mask_utils import (
-    _prepare_4d_causal_attention_mask,
-)
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from ipex_llm.ggml.quantize import ggml_tensor_qtype
-from ipex_llm.utils.common import invalidInputError
-from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
-from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, is_enough_kv_cache_room_4_36
-from ipex_llm.transformers.models.utils import should_use_fuse_rope
-from ipex_llm.transformers.models.utils import use_decoding_fast_path
-from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp
-from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU
-from ipex_llm.transformers.low_bit_linear import IQ2_XXS
-
-import os
-
-KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
-
-
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
-    The hidden states go from (batch, num_key_value_heads, seqlen, head_dim)
-    to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads,
-                                                           n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-def mixtral_moeblock_forward(self,
-                             hidden_states: torch.Tensor):
-    batch_size, sequence_length, hidden_dim = hidden_states.shape
-    hidden_states = hidden_states.view(-1, hidden_dim)
-    bs = hidden_states.shape[0]
-    # router_logits: (batch * sequence_length, n_experts)
-    router_logits = self.gate(hidden_states)
-
-    routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-    routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
-    routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-    # we cast back to the input dtype
-    routing_weights = routing_weights.to(hidden_states.dtype)
-
-    if bs == 1:
-        selected_experts = selected_experts[0].cpu().tolist()
-        for idx in range(self.top_k):
-            exp_id = selected_experts[idx]
-            expert_layer = self.experts[exp_id]
-            weight = routing_weights[:, idx]
-            if idx == 0:
-                final_hidden_states = expert_layer(hidden_states, weight)
-            else:
-                final_hidden_states = final_hidden_states + expert_layer(hidden_states, weight)
-    elif bs < 256 and hidden_states.device.type == 'xpu':
-        final_hidden_states = torch.zeros((batch_size * sequence_length, hidden_dim),
-                                          dtype=hidden_states.dtype, device=hidden_states.device)
-        import xe_linear
-        indexes = xe_linear.get_moe_indexes(selected_experts.to(torch.int32).cpu(), 8)
-        for expert_idx in range(self.num_experts):
-            expert_layer = self.experts[expert_idx]
-            idx_list = indexes[0][expert_idx]
-            top_x_list = indexes[1][expert_idx]
-            if len(idx_list) == 0:
-                continue
-
-            top_x = torch.tensor(top_x_list, dtype=torch.long, device=hidden_states.device)
-            current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
-            current_hidden_states = expert_layer(current_state,
-                                                 routing_weights[top_x_list, idx_list, None])
-            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
-    else:
-        final_hidden_states = torch.zeros(
-            (batch_size * sequence_length, hidden_dim),
-            dtype=hidden_states.dtype,
-            device=hidden_states.device
-        )
-        # One hot encode the selected experts to create an expert mask
-        # this will be used to easily index which expert is going to be sollicitated
-        expert_mask = torch.nn.functional.one_hot(selected_experts,
-                                                  num_classes=self.num_experts).permute(2, 1, 0)
-
-        # Loop over all available experts in the model and perform the computation on each expert
-        for expert_idx in range(self.num_experts):
-            expert_layer = self.experts[expert_idx]
-            idx, top_x = torch.where(expert_mask[expert_idx])
-
-            if top_x.shape[0] == 0:
-                continue
-
-            # in torch it is faster to index using lists than torch tensors
-            top_x_list = top_x.tolist()
-            idx_list = idx.tolist()
-
-            # Index the correct hidden states and compute the expert hidden state for
-            # the current expert. We need to make sure to multiply the output hidden
-            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
-            current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
-            current_hidden_states = expert_layer(current_state,
-                                                 routing_weights[top_x_list, idx_list, None])
-
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
-
-    final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
-    return final_hidden_states, router_logits
-
-
-def mixtral_attention_forward(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor]=None,
-    position_ids: Optional[torch.LongTensor]=None,
-    past_key_value: Optional[Tuple[torch.Tensor]]=None,
-    output_attentions: bool=False,
-    use_cache: bool=False,
-    padding_mask: Optional[torch.Tensor]=None,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    bsz, q_len, _ = hidden_states.size()
-    device = hidden_states.device
-    # for flash attention
-    original_dtype = hidden_states.dtype
-
-    use_fuse_rope = should_use_fuse_rope(hidden_states, position_ids, self.training)
-    enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
-    decoding_fast_path = use_decoding_fast_path(self.q_proj,
-                                                use_fuse_rope,
-                                                enough_kv_room,
-                                                bsz * q_len)
-
-    if decoding_fast_path:
-        hidden_states = hidden_states.view(1, -1)
-        cache_k = past_key_value.key_cache[self.layer_idx]
-        cache_v = past_key_value.value_cache[self.layer_idx]
-        kv_seq_len = cache_k.shape[-2]
-        import xe_linear
-        query_states, key_states, value_states = xe_linear.forward_qkv(hidden_states,
-                                                                       self.q_proj.weight,
-                                                                       self.k_proj.weight,
-                                                                       self.v_proj.weight,
-                                                                       position_ids,
-                                                                       cache_k, cache_v,
-                                                                       self.q_proj.weight.qtype,
-                                                                       self.v_proj.weight.qtype,
-                                                                       kv_seq_len,
-                                                                       self.head_dim,
-                                                                       self.rotary_emb.base,)
-        kv_seq_len += 1
-        # update past_key_value's seem_tokens and kv caches.
-        if self.layer_idx == 0:
-            past_key_value.seen_tokens = kv_seq_len
-        past_key_value.key_cache[self.layer_idx] = key_states
-        past_key_value.value_cache[self.layer_idx] = value_states
-    # diasble it for now as it will cause output change for unknown reason
-    # elif decoding_fast_path and self.q_proj.qtype == IQ2_XXS:
-    #     # this path self.v_proj use q4_0
-    #     hidden_states = hidden_states.view(1, -1)
-    #     cache_k = past_key_value.key_cache[self.layer_idx]
-    #     cache_v = past_key_value.value_cache[self.layer_idx]
-    #     kv_seq_len = cache_k.shape[-2]
-    #     import xe_linear
-    #     query_states, key_states = xe_linear.forward_qk(hidden_states,
-    #                                                       self.q_proj.weight,
-    #                                                       self.k_proj.weight,
-    #                                                       position_ids,
-    #                                                       cache_k,
-    #                                                       self.q_proj.weight.qtype,
-    #                                                       kv_seq_len,
-    #                                                       self.head_dim,
-    #                                                       10000)
-    #     kv_seq_len += 1
-    #     # update past_key_value's seem_tokens and kv caches.
-    #     if self.layer_idx == 0:
-    #         past_key_value.seen_tokens = kv_seq_len
-    #     # update value_states
-    #     value_states = self.v_proj(hidden_states)
-    #     value_states = value_states.view(bsz, q_len,
-    #                                      self.num_key_value_heads, self.head_dim).transpose(1, 2)
-    #     new_size = (cache_v.size(0),
-    #                 cache_v.size(1),
-    #                 cache_v.size(2) + value_states.size(2),
-    #                 cache_v.size(3))
-    #     new_cache_v = cache_v.as_strided(new_size, cache_v.stride(), storage_offset=0)
-    #     new_cache_v[:, :, cache_v.size(2):cache_v.size(2)+value_states.size(2), :] = value_states
-
-    #     past_key_value.key_cache[self.layer_idx] = key_states
-    #     past_key_value.value_cache[self.layer_idx] = new_cache_v
-    else:
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        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)
-
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            if self.layer_idx is None:
-                invalidInputError(False,
-                                  "The cache structure has changed since version v4.36. "
-                                  f"If you are using {self.__class__.__name__} for "
-                                  "auto-regressive decodingwith k/v caching, please make sure "
-                                  "to initialize the attention class with a layer index.")
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-
-        if use_fuse_rope:
-            import xe_addons
-            xe_addons.rotary_half_inplaced(self.rotary_emb.inv_freq, position_ids,
-                                           query_states, key_states)
-        else:
-            cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-            query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
-                                                            cos, sin, position_ids, "mixtral")
-
-        if past_key_value is not None:
-            # update the number of seen tokens
-            if self.layer_idx == 0:
-                past_key_value.seen_tokens += key_states.shape[-2]
-
-            # reuse k, v, self_attention
-            # update `past_key_value` with `key_states` and `value_states` for layer `layer_idx`
-            if len(past_key_value.key_cache) <= self.layer_idx:
-                past_key_value.key_cache.append(key_states)
-                past_key_value.value_cache.append(value_states)
-            else:
-                cache_k = past_key_value.key_cache[self.layer_idx]
-                cache_v = past_key_value.value_cache[self.layer_idx]
-
-                if not enough_kv_room:
-                    # allocate new
-                    new_c_k, new_c_v = extend_kv_cache(bsz,
-                                                       self.num_key_value_heads,  # Support GQA
-                                                       self.head_dim,
-                                                       cache_k.size(2),
-                                                       kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
-                                                       dtype=cache_k.dtype,
-                                                       device=device)
-
-                    new_c_k[:] = cache_k
-                    new_c_v[:] = cache_v
-                    cache_k = new_c_k
-                    cache_v = new_c_v
-
-                key_states, value_states = append_kv_cache(cache_k,
-                                                           cache_v,
-                                                           key_states,
-                                                           value_states)
-
-                # update past_key_value
-                past_key_value.key_cache[self.layer_idx] = key_states
-                past_key_value.value_cache[self.layer_idx] = value_states
-
-    if not self.training and not hidden_states.requires_grad:
-        fsdp_flag = use_flash_attention(query_states, key_states)
-    else:
-        fsdp_flag = False
-    if fsdp_flag:
-        attention_dtype = torch.float16  # use fp16 for flash attention
-    else:
-        attention_dtype = original_dtype
-
-    # repeat k/v heads if n_kv_heads < n_heads
-    key_states = repeat_kv(key_states, self.num_key_value_groups).to(device,
-                                                                     dtype=attention_dtype)
-    value_states = repeat_kv(value_states, self.num_key_value_groups).to(device,
-                                                                         dtype=attention_dtype)
-
-    if fsdp_flag:
-        attn_output = F.scaled_dot_product_attention(query_states.to(dtype=attention_dtype),
-                                                     key_states,
-                                                     value_states,
-                                                     is_causal=True)
-        attn_weights = None
-    elif use_sdp(query_states.shape[2], key_states.shape[2], self.head_dim, query_states):
-        import xe_addons
-        attn_output = xe_addons.sdp(query_states, key_states, value_states, attention_mask)
-        attn_output = attn_output.view(query_states.shape)
-        attn_weights = None
-    else:
-        attn_weights = torch.matmul(
-            query_states.to(key_states.dtype),
-            key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-            invalidInputError(
-                False,
-                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)},"
-                f" but is {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-                invalidInputError(
-                    False,
-                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)},"
-                    f" but is {attention_mask.size()}"
-                )
-
-            attn_weights = attn_weights + attention_mask
-
-        # upcast attention to fp32
-        attn_weights = nn.functional.\
-            softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
-        attn_output = torch.matmul(attn_weights, value_states)
-
-    if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-        invalidInputError(
-            False,
-            f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)},"
-            f" but is {attn_output.size()}"
-        )
-
-    attn_output = attn_output.transpose(1, 2).contiguous()
-    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-    attn_output = self.o_proj(attn_output)
-
-    if not output_attentions:
-        attn_weights = None
-
-    return attn_output, attn_weights, past_key_value
-
-
-def mixtral_mlp_forward(
-    self,
-    x: torch.Tensor,
-    routing_weights
-) -> torch.Tensor:
-    qtype = getattr(self.w1, "qtype", None)
-    if mlp_fusion_check(x, qtype, self.training):
-        import xe_linear
-        return self.w2(xe_linear.mlp_forward_xpu(
-            x, self.w1.weight.data, self.w3.weight.data,
-            x.shape[0], x.shape[1], self.w1.out_len,
-            SILU, qtype,
-        )) * routing_weights
-    else:
-        current_hidden_states = self.act_fn(self.w1(x)) * self.w3(x)
-        current_hidden_states = self.w2(current_hidden_states)
-        return routing_weights * current_hidden_states
-
-
-def mixtral_model_forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_router_logits: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-) -> Union[Tuple, MoeModelOutputWithPast]:
-    # to be compatible with transformers>=4.37.0
-    self._use_flash_attention_2 = self.config._attn_implementation == "flash_attention_2"
-
-    output_attentions = output_attentions if output_attentions is not None \
-        else self.config.output_attentions
-    output_router_logits = (
-        output_router_logits if output_router_logits is not None
-        else self.config.output_router_logits
-    )
-    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
-
-    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-    # retrieve input_ids and inputs_embeds
-    if input_ids is not None and inputs_embeds is not None:
-        invalidInputError(False, "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")  # noqa
-    elif input_ids is not None:
-        batch_size, seq_length = input_ids.shape
-    elif inputs_embeds is not None:
-        batch_size, seq_length, _ = inputs_embeds.shape
-    else:
-        invalidInputError(False, "You have to specify either decoder_input_ids or decoder_inputs_embeds")  # noqa
-
-    past_key_values_length = 0
-
-    if use_cache:
-        use_legacy_cache = not isinstance(past_key_values, Cache)
-        if use_legacy_cache:
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-        past_key_values_length = past_key_values.get_usable_length(seq_length)
-
-    if position_ids is None:
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-        position_ids = torch.arange(
-            past_key_values_length, seq_length + past_key_values_length,
-            dtype=torch.long, device=device
-        )
-        position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-    else:
-        position_ids = position_ids.view(-1, seq_length).long()
-
-    if inputs_embeds is None:
-        inputs_embeds = self.embed_tokens(input_ids)
-
-    if attention_mask is not None and self._use_flash_attention_2 and use_cache:
-        is_padding_right = attention_mask[:, -1].sum().item() != batch_size
-        if is_padding_right:
-            invalidInputError(
-                False,
-                "You are attempting to perform batched generation with padding_side='right'"
-                " this may lead to unexpected behaviour for Flash Attention version of Mixtral. Make sure to "  # noqa
-                " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
-            )
-
-    if self._use_flash_attention_2:
-        # 2d mask is passed through the layers
-        attention_mask = attention_mask \
-            if (attention_mask is not None and 0 in attention_mask) else None
-    else:
-        # 4d mask is passed through the layers
-        attention_mask = _prepare_4d_causal_attention_mask(
-            attention_mask,
-            (batch_size, seq_length),
-            inputs_embeds,
-            past_key_values_length,
-            sliding_window=self.config.sliding_window,
-        )
-
-    hidden_states = inputs_embeds
-
-    if self.gradient_checkpointing and self.training:
-        if use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."  # noqa
-            )
-            use_cache = False
-
-    # decoder layers
-    all_hidden_states = () if output_hidden_states else None
-    all_self_attns = () if output_attentions else None
-    all_router_logits = () if output_router_logits else None
-    next_decoder_cache = None
-
-    for decoder_layer in self.layers:
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        if self.gradient_checkpointing and self.training:
-            layer_outputs = self._gradient_checkpointing_func(
-                decoder_layer.__call__,
-                hidden_states,
-                attention_mask,
-                position_ids,
-                past_key_values,
-                output_attentions,
-                output_router_logits,
-                use_cache,
-            )
-        else:
-            # bigdl-llm changes:
-            #
-            # Avoid moving `attention_mask`` and `position_ids`` to other devices multiple times.
-            #
-            # When the model is partitioned on two different devices using
-            # `accelerate`'s `dispatch``, a hook to move inputs to the correct device is
-            # added to each layer's `forward``, which will result in moving `attention_mask`
-            # and `position_ids`, which allocated on device:0, to other devices for each
-            # decoder layer not in device:0.
-            #
-            # To avoid this, we move `attention_mask` and `position_ids` to the device of
-            # the current layer before the forward call, so that the moving is only done once
-            # for each devices other than devie:0.
-            #
-            curr_device = decoder_layer.input_layernorm.weight.device
-            if attention_mask is not None:
-                attention_mask = attention_mask.to(curr_device)
-            if position_ids is not None:
-                position_ids = position_ids.to(curr_device)
-            # bigdl-llm changes end
-            layer_outputs = decoder_layer(
-                hidden_states,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_value=past_key_values,
-                output_attentions=output_attentions,
-                output_router_logits=output_router_logits,
-                use_cache=use_cache,
-            )
-
-        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],)
-
-        if output_router_logits:
-            all_router_logits += (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 = None
-    if use_cache:
-        next_cache = next_decoder_cache.to_legacy_cache() \
-            if use_legacy_cache else next_decoder_cache
-
-    if not return_dict:
-        return tuple(
-            v
-            for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits]  # noqa
-            if v is not None
-        )
-    return MoeModelOutputWithPast(
-        last_hidden_state=hidden_states,
-        past_key_values=next_cache,
-        hidden_states=all_hidden_states,
-        attentions=all_self_attns,
-        router_logits=all_router_logits,
-    )

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

@@ -36,7 +36,7 @@ import math
 import torch
 from typing import Optional
 
-from ipex_llm.transformers.utils import get_xpu_device_type
+from ipex_llm.transformers.utils import get_xpu_device_name
 from ipex_llm.transformers.models.common import padding_qkv_hd
 from ipex_llm.transformers.models.common import scaled_dot_product_attention
 from diffusers.models.attention_processor import Attention
@@ -144,7 +144,7 @@ class AttnProcessor2_0:
 
 def upcast_vae(self):
     # workaround overflow and ipex's bugs
-    if get_xpu_device_type(self.vae.post_quant_conv.weight) in ["arc", "flex", "pvc"]:
+    if get_xpu_device_name(self.vae.post_quant_conv.weight.device) == "arc":
         self.vae.to(torch.bfloat16)
     else:
         self.vae.decoder.up_blocks.to(torch.bfloat16)

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

@@ -19,7 +19,7 @@ import torch
 import warnings
 from ipex_llm.utils.common import invalidInputError
 from ipex_llm.ggml.quantize import ggml_tensor_qtype
-from ipex_llm.transformers.utils import get_ipex_version, get_xpu_device_type
+from ipex_llm.transformers.utils import get_ipex_version, get_xpu_device_name
 from ipex_llm.transformers.low_bit_linear import SYM_INT4, SYM_INT8, FP8E5, IQ2_XXS, FP4, FP8E4,\
     FP6, ASYM_INT4
 
@@ -85,16 +85,14 @@ def use_quantize_kv_cache(linear: torch.nn.Module, x: torch.Tensor, kv_group: in
         return os.environ["IPEX_LLM_QUANTIZE_KV_CACHE"] == "1"
     elif os.environ.get("IPEX_LLM_LOW_MEM", None) is not None:
         return os.environ["IPEX_LLM_LOW_MEM"] == "1"
+    elif linear.qtype in [ggml_tensor_qtype["fp16"], ggml_tensor_qtype["bf16"]]:
+        return False
     else:
-        return x.device.type == 'xpu' and kv_cache_device_check(x, kv_group) \
+        device_name = get_xpu_device_name(x.device)
-            and hasattr(linear, "qtype") and \
+        return (
-            linear.qtype != ggml_tensor_qtype["fp16"] and linear.qtype != ggml_tensor_qtype["bf16"]
+            device_name in ["mtl", "lnl", "arl"] and kv_group == 1
-
+            or device_name in ["arc", "bmg"] and x.size(0) > 1
-
+        )
-def kv_cache_device_check(x: torch.Tensor, kv_group: int) -> bool:
-    return (get_xpu_device_type(x) in ["mtl", "lnl"] and kv_group <= 1) or \
-        ((get_xpu_device_type(x) == "arc" or get_xpu_device_type(x) == "flex") and
-            1 < x.size(0) and x.size(0) <= 8)
 
 
 def init_fp8_kv_cache(batch_size, num_heads, current_length, head_dim, device):
@@ -226,57 +224,6 @@ def is_enough_kv_cache_room_4_31(past_key_value, seq_len=1):
         (past_key_value[0].size(2) + seq_len) * past_key_value[0].size(3)
 
 
-def use_flash_attention(query, key, attention_mask=None):
-    # here we support query's shape is always [batch_size, head_num, q_len, head_dim],
-    # key's shape is always [batch_size, head_num, k_len, head_dim]
-    invalidInputError(query.dim() == 4,
-                      "Here query input of use_flash_attention should be [batch_size, "
-                      "head_num, q_len, head_dim]")
-    invalidInputError(key.dim() == 4,
-                      "Here key input of use_flash_attention should be [batch_size, "
-                      "head_num, k_len, head_dim]")
-    bsz, _, q_len, _ = query.size()
-    k_len = key.size()[2]
-    # check whether ipex flash attention can be used
-    if q_len != k_len:
-        # now only use flash attention for first token
-        # as it seems have no performance benifit for rest token now
-        return False
-    if query.device.type != "xpu":
-        # ipex flash attention only support for xpu
-        return False
-    ipex_version = get_ipex_version()
-    if ipex_version <= "2.0.110+xpu":
-        # ipex flash attention is supported from ipex 2.1
-        return False
-    if not torch.xpu.has_xetla():
-        # ipex flash attention is only supported for xetla
-        # may update this later
-        return False
-    elif get_xpu_device_type(query) != "pvc":
-        return False
-    if query.dtype not in [torch.float32, torch.float16]:
-        # only use flash attention for fp32/fp16 input
-        return False
-    if bsz > 1:
-        # as flash attention doesn't support attn_mask in ipex 2.1,
-        # so it will cause output error for padded batch input
-        if attention_mask is None:
-            return True
-        else:
-            # TODO: below logic may change for different model
-            # attention mask shape : [bsz, 1, q_len, k_len]
-            if attention_mask[0].squeeze()[0, 0].item() != 0:
-                # first batch contains padding
-                # otherwise we suppose it should be a upper triangular matrix
-                # at the same time, the diagonal is also 0
-                return False
-            elif not attention_mask.equal(attention_mask[0].repeat(bsz, 1, 1, 1)):
-                # check whether mask of every batch is the same
-                return False
-    return True
-
-
 def use_sdp(q_len, kv_len, head_dim, query_states):
     return (
         query_states.device.type == "xpu"
@@ -315,38 +262,16 @@ def mlp_fusion_check(x, qtype, training):
     if training or x.requires_grad:
         return False
     if qtype == FP6:
-        device = get_xpu_device_type(x)
+        device = get_xpu_device_name(x.device)
-        if device in ["mtl", "lnl"]:
+        if device in ["mtl", "lnl", "arl"]:
-            return False
-    return True
-
-
-def use_decoding_fast_path(proj,
-                           use_fuse_rope,
-                           enough_kv_room,
-                           bs,
-                           qtype_check=decoding_fast_path_qtype_check):
-    if proj is None:
-        return False
-    device = get_xpu_device_type(proj.weight)
-    if not qtype_check(proj):
-        return False
-    if not use_fuse_rope:
-        return False
-    if not enough_kv_room:
-        return False
-    if bs != 1:
-        return False
-
-    if device in ["uhd"]:
             return False
     return True
 
 
 def use_xmx(x: torch.Tensor, qtype: int):
-    device = get_xpu_device_type(x)
+    device = get_xpu_device_name(x.device)
     return (
-        device in ["arc", "flex", "pvc"]
+        device in ["arc", "pvc"]
         and qtype in [SYM_INT4, SYM_INT8, FP8E4, FP8E5]
         and (
             (device == "pvc" and 1 < x.size(0) <= 16)
@@ -370,7 +295,7 @@ def fp16_fusion_check(proj, x, training):
         return False
     if x.requires_grad:
         return False
-    device_type = get_xpu_device_type(x)
+    device_type = get_xpu_device_name(x.device)
     if device_type != "pvc":
         return False
     return True
@@ -439,7 +364,7 @@ def should_use_compresskv(x: torch.Tensor, prompt_len: int):
     else:
         if use_compress_kv is None:
             return (
-                get_xpu_device_type(x) in ["mtl", "lnl"]
+                get_xpu_device_name(x.device) in ["mtl", "lnl", "arl"]
                 and prompt_len >= 1800
                 and prompt_len <= 4500
             )

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

@@ -168,27 +168,12 @@ def get_ipex_version():
     return _ipex_version
 
 
-def get_xpu_device_type(x):
+def get_xpu_device_name(device: torch.device):
-    if x.device.type != "xpu":
+    if device.type != "xpu":
-        return x.device.type
+        return device.type
-    name = torch.xpu.get_device_name(x.device.index)
-    if name.startswith("Intel(R) Arc(TM) A"):
-        return "arc"
-    elif name.startswith("Intel(R) Graphics [0xe20b]"):
-        return "bmg"
-    elif name.startswith("Intel(R) Arc(TM)"):
-        if 'V' in name:
-            return "lnl"
     else:
-            return "mtl"
+        import xe_linear
-    elif name.startswith("Intel(R) Data Center GPU Flex"):
+        return xe_linear.get_xpu_device_name(device)
-        return "flex"
-    elif name.startswith("Intel(R) Data Center GPU Max"):
-        return "pvc"
-    elif name.startswith("Intel(R) UHD"):
-        return "uhd"
-    else:
-        return "others"
 
 
 def load_imatrix_data(imatrix_file):