From 191b18434182edf4114b3ec7ece42bd1cb99f4d6 Mon Sep 17 00:00:00 2001
From: "Wang, Jian4" <61138589+hzjane@users.noreply.github.com>
Date: Tue, 7 May 2024 10:19:50 +0800
Subject: [PATCH] LLM: Optimize cohere model (#10878)

* use mlp and rms

* optimize kv_cache

* add fuse qkv

* add flash attention and fp16 sdp

* error fp8 sdp

* fix optimized

* fix style

* update

* add for pp
---
 .../llm/src/ipex_llm/transformers/convert.py  |  18 +
 .../ipex_llm/transformers/models/cohere.py    | 457 ++++++++++++++++++
 2 files changed, 475 insertions(+)
 create mode 100644 python/llm/src/ipex_llm/transformers/models/cohere.py

diff --git a/python/llm/src/ipex_llm/transformers/convert.py b/python/llm/src/ipex_llm/transformers/convert.py
index 553e4e6c..dcd4597a 100644
--- a/python/llm/src/ipex_llm/transformers/convert.py
+++ b/python/llm/src/ipex_llm/transformers/convert.py
@@ -1282,6 +1282,24 @@ def _optimize_post(model, lightweight_bmm=False):
         convert_forward(model,
                         module.Qwen2MoeAttention,
                         qwen2moe_attention_forward)
+    elif model.config.model_type == "cohere":
+        # for CohereForAI/c4ai-command-r-v01
+        modeling_module_name = model.__class__.__module__
+        module = importlib.import_module(modeling_module_name)
+        from ipex_llm.transformers.models.cohere import cohere_attention_forward
+        from ipex_llm.transformers.models.cohere import cohere_model_forward
+        convert_forward(model,
+                        module.CohereModel,
+                        cohere_model_forward)
+        convert_forward(model,
+                        module.CohereAttention,
+                        cohere_attention_forward)
+        convert_forward(model,
+                        module.CohereLayerNorm,
+                        llama_rms_norm_forward)
+        convert_forward(model,
+                        module.CohereMLP,
+                        llama_mlp_forward)
     elif model.config.model_type == "aquila":
         modeling_module_name = model.__class__.__module__
         module = importlib.import_module(modeling_module_name)
diff --git a/python/llm/src/ipex_llm/transformers/models/cohere.py b/python/llm/src/ipex_llm/transformers/models/cohere.py
new file mode 100644
index 00000000..f5404665
--- /dev/null
+++ b/python/llm/src/ipex_llm/transformers/models/cohere.py
@@ -0,0 +1,457 @@
+#
+# 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.llama 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_esimd_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.qwen2 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_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 linear_q4_0
+        attn_output = linear_q4_0.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(self, hidden_states, position_ids)
+    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 linear_q4_0
+        query_states, key_states, value_states = linear_q4_0.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_esimd_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
+        import linear_q4_0
+        attn_output = linear_q4_0.sdp_fp16(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,
+                                    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