add basic glm-edge support

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

@@ -1070,6 +1070,10 @@ def _optimize_pre(model, qtype=None):
                     model.apply(split_mlp)
             elif model.config.num_layers in [40, 28]:
                 model.apply(split_mlp)
+    elif model.config.model_type == "glm":
+        from ipex_llm.transformers.models.glm import merge_qkv, split_mlp
+        model.apply(merge_qkv)
+        model.apply(split_mlp)
 
     return model
 
@@ -1487,7 +1491,19 @@ def _optimize_post(model, lightweight_bmm=False):
                     # workaround glm4-9b fp16 overflow
                     from ipex_llm.transformers.models.chatglm4 import chatglm4_block_forward
                     convert_forward(model, module.GLMBlock, chatglm4_block_forward)
-
+    elif model.config.model_type == "glm":
+        # glm-edge series
+        modeling_module_name = model.__class__.__module__
+        module = importlib.import_module(modeling_module_name)
+        from ipex_llm.transformers.models.common import rms_norm_forward
+        from ipex_llm.transformers.models.common import mlp_silu_forward
+        from ipex_llm.transformers.models.glm import glm_attention_forward
+        from ipex_llm.transformers.models.glm import glm_model_forward_wrapper
+        convert_forward(model, module.GlmRMSNorm, rms_norm_forward)
+        convert_forward(model, module.GlmMLP, mlp_silu_forward)
+        convert_forward(model, module.GlmAttention, glm_attention_forward)
+        glm_model_forward = glm_model_forward_wrapper(module.GlmModel.forward)
+        convert_forward(model, module.GlmModel, glm_model_forward)
     elif "mpt" in model.config.model_type:
         if model.config.architectures is not None:
             modeling_module_name = model.__class__.__module__

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

@@ -0,0 +1,202 @@
+#
+# 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.
+#
+# This file is adapted from
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/glm/modeling_glm.py
+#
+# which is licensed under Apache License 2.0:
+#
+# Copyright 2024 The GLM & ZhipuAI team and HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+
+from typing import Optional, Tuple
+from transformers.cache_utils import Cache
+from transformers.models.glm.modeling_glm import GlmAttention, GlmMLP
+from transformers.models.glm.modeling_glm import repeat_kv, apply_rotary_pos_emb
+from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache
+from ipex_llm.transformers.models.common import merge_qkv_base
+from ipex_llm.transformers.models.utils import use_sdp, use_sdp_causal
+from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
+
+
+def merge_qkv(module: torch.nn.Module):
+    merge_qkv_base(module, GlmAttention)
+
+
+def split_mlp(module: torch.nn.Module):
+    if isinstance(module, GlmMLP):
+        gate_weight, up_weight = module.gate_up_proj.weight.data.chunk(2, dim=0)
+
+        gate_proj = torch.nn.Linear(0, 0, bias=False)
+        gate_proj.weight = torch.nn.Parameter(gate_weight, requires_grad=False)
+        gate_proj.in_features = gate_weight.size(1)
+        gate_proj.out_features = gate_weight.size(0)
+
+        up_proj = torch.nn.Linear(0, 0, bias=False)
+        up_proj.weight = torch.nn.Parameter(up_weight, requires_grad=False)
+        up_proj.in_features = up_weight.size(1)
+        up_proj.out_features = up_weight.size(0)
+
+        module.gate_proj = gate_proj
+        module.up_proj = up_proj
+
+        del module.gate_up_proj
+
+        # rename activation function
+        module.act_fn = module.activation_fn
+
+
+def glm_attention_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Cache] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+    cache_position: Optional[torch.LongTensor] = None,
+    position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
+    **kwargs,
+):
+    bsz, q_len, _ = hidden_states.size()
+
+    qkv = self.qkv_proj(hidden_states)
+    qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
+    qkv = qkv.transpose(1, 2)
+    query_states, key_states, value_states = qkv.split([self.num_heads,
+                                                        self.num_key_value_heads,
+                                                        self.num_key_value_heads], dim=1)
+
+    cos, sin = position_embeddings
+    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+    use_quantizekv = isinstance(past_key_value, DynamicFp8Cache)
+    # sin and cos are specific to RoPE models; cache_position 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)
+
+    kv_seq_len = key_states.size(-2)
+    if attention_mask is not None:  # no matter the length, we just slice it
+        attention_mask = attention_mask[:, :, :, : kv_seq_len]
+
+    if use_sdp(q_len, kv_seq_len, self.head_dim, query_states):
+        import xe_addons
+        if use_quantizekv:
+            attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states,
+                                            attention_mask)
+        else:
+            attn_output = xe_addons.sdp(query_states, key_states, value_states,
+                                        attention_mask)
+    elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training):
+        import xe_addons
+        if use_quantizekv:
+            attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
+                                                   value_states, attention_mask)
+        else:
+            attn_output = xe_addons.sdp_causal(query_states, key_states,
+                                               value_states, attention_mask)
+    else:
+        if use_quantizekv:
+            key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
+                                                            query_states.dtype)
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states,
+                                    key_states.transpose(2, 3)) * self.scaling
+
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
+                                                   dtype=torch.float32).to(query_states.dtype)
+        attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout,
+                                                   training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.reshape(bsz, q_len, 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 glm_model_forward_wrapper(origin_forward):
+    def glm_model_forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **flash_attn_kwargs,
+    ):
+        # ipex-llm changes start
+        # IPEX-LLM OPT: kv cache and quantize kv cache
+        inputs = input_ids if input_ids is not None else inputs_embeds
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        use_cache = use_cache or inputs.device.type == 'xpu'
+        use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.down_proj, inputs,
+                                                self.config.num_attention_heads //
+                                                self.config.num_key_value_heads)
+
+        if use_cache:
+            if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
+                past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
+            elif not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache):
+                past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
+        # ipex-llm changes end
+
+        return origin_forward(
+            self=self,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+            **flash_attn_kwargs,
+        )
+
+    return glm_model_forward