[LLM] First commit the llm deployment code

fastdeploy/model_executor/models/qwen2.py

@@ -0,0 +1,426 @@
+"""
+# Copyright (c) 2024 PaddlePaddle Authors. 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.
+"""
+
+from __future__ import annotations
+
+from functools import partial
+
+import paddle
+from paddle import nn
+from paddlenlp.transformers import PretrainedModel
+
+from fastdeploy.config import LLMConfig, ModelConfig
+from fastdeploy.model_executor.layers.activation import SiluAndMul
+from fastdeploy.model_executor.layers.attention import Attention
+from fastdeploy.model_executor.layers.embeddings import VocabParallelEmbedding
+from fastdeploy.model_executor.layers.linear import (
+    MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear)
+from fastdeploy.model_executor.layers.lm_head import ParallelLMHead
+from fastdeploy.model_executor.layers.normalization import RMSNorm
+from fastdeploy.model_executor.models.model_base import ModelForCasualLM
+from fastdeploy.worker.model_runner import ForwardMeta
+
+
+class Qwen2MLP(nn.Layer):
+    """
+    """
+
+    def __init__(
+        self,
+        llm_config: LLMConfig,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.nranks = llm_config.parallel_config.mp_size
+        self.gate_up_proj = MergedColumnParallelLinear(
+            llm_config=llm_config,
+            prefix=f"{prefix}.up_gate_proj",
+            with_bias=False,
+            activation=llm_config.model_config.hidden_act,
+            use_fast_ffn=True,
+        )
+
+        self.down_proj = RowParallelLinear(
+            llm_config=llm_config,
+            prefix=f"{prefix}.down_proj",
+            input_size=(llm_config.model_config.ffn_hidden_size //
+                        self.nranks),
+            output_size=llm_config.model_config.hidden_size,
+            with_bias=False,
+        )
+
+        self.act_fn = SiluAndMul(
+            llm_config=llm_config,
+            bias=getattr(self.gate_up_proj, "linear_bias", None),
+            act_method=llm_config.model_config.hidden_act,
+        )
+
+    def load_state_dict(self, state_dict):
+        """
+        """
+        self.gate_up_proj.load_state_dict(state_dict)
+        self.down_proj.load_state_dict(state_dict)
+
+    def forward(self, x):
+        """
+        """
+        gate_up_out = self.gate_up_proj(x)
+        act_out = self.act_fn(gate_up_out)
+        down_out = self.down_proj(act_out)
+        return down_out
+
+
+class Qwen2Attention(nn.Layer):
+    """
+    """
+
+    def __init__(self,
+                 llm_config: LLMConfig,
+                 layer_id: int,
+                 prefix: str = "") -> None:
+        super().__init__()
+
+        nranks = llm_config.parallel_config.mp_size
+
+        self.qkv_proj = QKVParallelLinear(llm_config=llm_config,
+                                          prefix=f"{prefix}.qkv_proj",
+                                          with_bias=True)
+
+        self.o_proj = RowParallelLinear(
+            llm_config=llm_config,
+            prefix=f"{prefix}.o_proj",
+            input_size=(llm_config.model_config.hidden_size // nranks),
+            output_size=llm_config.model_config.hidden_size,
+        )
+
+        self.attn = Attention(llm_config=llm_config,
+                              layer_id=layer_id,
+                              prefix=prefix,
+                              use_neox_rotary_style=True)
+
+    def load_state_dict(self, state_dict):
+        """
+        """
+        self.qkv_proj.load_state_dict(state_dict)
+        self.o_proj.load_state_dict(state_dict)
+
+    def forward(
+        self,
+        forward_meta: ForwardMeta,
+        hidden_states: paddle.Tensor,
+    ):
+        """
+        """
+        qkv_out = self.qkv_proj(hidden_states)
+
+        atten_out = self.attn(
+            qkv=qkv_out,
+            forward_meta=forward_meta,
+        )
+        output = self.o_proj(atten_out)
+        return output
+
+
+class Qwen2DecoderLayer(nn.Layer):
+    """
+    """
+
+    def __init__(
+        self,
+        llm_config: LLMConfig,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        layer_id = int(prefix.split(sep='.')[-1])
+
+        self.self_attn = Qwen2Attention(
+            llm_config=llm_config,
+            layer_id=layer_id,
+            prefix=f"{prefix}.self_attn",
+        )
+
+        self.mlp = Qwen2MLP(
+            llm_config=llm_config,
+            prefix=f"{prefix}.mlp",
+        )
+
+        self.input_layernorm = RMSNorm(
+            llm_config,
+            hidden_size=llm_config.model_config.hidden_size,
+            eps=1e-6,
+            prefix=f"{prefix}.input_layernorm",
+        )
+
+        self.post_attention_layernorm = RMSNorm(
+            llm_config,
+            hidden_size=llm_config.model_config.hidden_size,
+            eps=1e-6,
+            prefix=f"{prefix}.post_attention_layernorm",
+        )
+
+    def load_state_dict(self, state_dict):
+        """
+        """
+        self.self_attn.load_state_dict(state_dict)
+        self.mlp.load_state_dict(state_dict)
+        self.input_layernorm.load_state_dict(state_dict)
+        self.post_attention_layernorm.load_state_dict(state_dict)
+
+    def forward(
+        self,
+        forward_meta: ForwardMeta,
+        hidden_states: paddle.Tensor,
+        residual: paddle.Tensor = None,
+    ):
+        """
+        """
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+
+        hidden_states = self.self_attn(
+            hidden_states=hidden_states,
+            forward_meta=forward_meta,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+
+        hidden_states = self.mlp(hidden_states)
+
+        return hidden_states, residual
+
+
+class Qwen2Model(nn.Layer):
+    """
+    """
+
+    def __init__(
+        self,
+        llm_config: LLMConfig = None,
+    ):
+        """
+        Initializer for the Qwen2Model class.
+
+        Args:
+
+        """
+        super().__init__()
+
+        self.num_layers = llm_config.model_config.num_layers
+        llm_config.model_config.prefix_name = "qwen2"
+
+        self.embeddings = VocabParallelEmbedding(
+            llm_config=llm_config,
+            num_embeddings=llm_config.model_config.vocab_size,
+            embedding_dim=llm_config.model_config.hidden_size,
+            params_dtype=paddle.get_default_dtype,
+            prefix=(f"{llm_config.model_config.prefix_name}.embed_tokens"),
+        )
+
+        self.layers = nn.LayerList([
+            Qwen2DecoderLayer(
+                llm_config=llm_config,
+                prefix=f"{llm_config.model_config.prefix_name}.layers.{i}")
+            for i in range(self.num_layers)
+        ])
+
+        self.norm = RMSNorm(
+            llm_config,
+            hidden_size=llm_config.model_config.hidden_size,
+            eps=1e-5,
+            prefix=f"{llm_config.model_config.prefix_name}.norm",
+        )
+
+    def load_state_dict(self, state_dict):
+        """
+        Load model parameters from a given state dictionary.
+
+        Args:
+            state_dict (dict[str, np.ndarray | paddle.Tensor]):
+                A dictionary containing model parameters, where keys are parameter names
+                and values are NumPy arrays or PaddlePaddle tensors.
+        """
+        self.embeddings.load_state_dict(state_dict)
+        self.norm.load_state_dict(state_dict)
+        for i in range(self.num_layers):
+            self.layers[i].load_state_dict(state_dict)
+
+    def forward(
+        self,
+        ids_remove_padding: paddle.Tensor,
+        forward_meta: ForwardMeta,
+    ):
+        """
+        """
+
+        hidden_states = self.embeddings(ids_remove_padding=ids_remove_padding)
+
+        residual = None
+
+        for i in range(self.num_layers):
+            hidden_states, residual = self.layers[i](forward_meta,
+                                                     hidden_states, residual)
+
+        hidden_states = hidden_states + residual
+
+        out = self.norm(hidden_states)
+
+        return out
+
+
+class Qwen2ForCausalLM(ModelForCasualLM):
+    """
+    Qwen2ForCausalLM
+    """
+
+    def __init__(self, llm_config: LLMConfig):
+        """
+        Args:
+            llm_config (LLMConfig): Configurations for the LLM model.
+        """
+        super(Qwen2ForCausalLM, self).__init__(llm_config)
+
+        self.model = Qwen2Model(llm_config=llm_config)
+
+        self.ori_vocab_size = llm_config.model_config.ori_vocab_size
+
+        self.lm_head = ParallelLMHead(
+            llm_config=llm_config,
+            embedding_dim=llm_config.model_config.hidden_size,
+            num_embeddings=llm_config.model_config.vocab_size,
+            prefix="lm_head",
+        )
+
+    @classmethod
+    def name(self):
+        """
+        """
+        return "Qwen2ForCausalLM"
+
+    @paddle.no_grad()
+    def set_state_dict(self, state_dict):
+        """
+        Load model parameters from a given state dictionary.
+
+        Args:
+            state_dict (dict[str, np.ndarray | paddle.Tensor]):
+                A dictionary containing model parameters, where keys are parameter names
+                and values are NumPy arrays or PaddlePaddle tensors.
+        """
+        self.model.load_state_dict(state_dict)
+        self.lm_head.load_state_dict(state_dict)
+
+    def compute_logits(self, hidden_states: paddle.Tensor):
+        """
+        """
+        logits = self.lm_head(hidden_states)
+        logits = paddle.cast(logits, paddle.float32)
+        logits[:, self.ori_vocab_size:] = -float("inf")
+
+        return logits
+
+    def forward(
+        self,
+        ids_remove_padding: paddle.Tensor,
+        forward_meta: ForwardMeta,
+    ):
+        """
+        """
+        hidden_states = self.model(ids_remove_padding, forward_meta)
+
+        return hidden_states
+
+
+class Qwen2PretrainedModel(PretrainedModel):
+    """
+    Qwen2PretrainedModel
+    """
+
+    config_class = LLMConfig
+
+    def _init_weight(self, layer):
+        """
+        _init_weight
+        """
+        return None
+
+    @classmethod
+    def _get_tensor_parallel_mappings(cls, config: ModelConfig, is_split=True):
+
+        from paddlenlp.transformers.conversion_utils import split_or_merge_func
+
+        fn = split_or_merge_func(
+            is_split=is_split,
+            tensor_parallel_degree=config.tensor_parallel_degree,
+            tensor_parallel_rank=config.tensor_parallel_rank,
+            num_attention_heads=config.num_attention_heads,
+        )
+
+        def get_tensor_parallel_split_mappings(num_layers):
+            final_actions = {}
+
+            base_actions = {
+                "lm_head.weight": partial(fn, is_column=True),
+                # Row Linear
+                "embed_tokens.weight": partial(fn, is_column=False),
+                "layers.0.self_attn.o_proj.weight": partial(fn,
+                                                            is_column=False),
+                "layers.0.mlp.down_proj.weight": partial(fn, is_column=False),
+            }
+
+            # Column Linear
+            if config.fuse_attention_qkv:
+                base_actions["layers.0.self_attn.qkv_proj.weight"] = partial(
+                    fn, is_column=True)
+            else:
+                base_actions["layers.0.self_attn.q_proj.weight"] = partial(
+                    fn, is_column=True)
+                base_actions["layers.0.self_attn.q_proj.bias"] = partial(
+                    fn, is_column=True)
+                # if we have enough num_key_value_heads to split, then split it.
+                if config.num_key_value_heads % config.tensor_parallel_degree == 0:
+                    base_actions["layers.0.self_attn.k_proj.weight"] = partial(
+                        fn, is_column=True)
+                    base_actions["layers.0.self_attn.v_proj.weight"] = partial(
+                        fn, is_column=True)
+                    base_actions["layers.0.self_attn.k_proj.bias"] = partial(
+                        fn, is_column=True)
+                    base_actions["layers.0.self_attn.v_proj.bias"] = partial(
+                        fn, is_column=True)
+
+            base_actions["layers.0.mlp.gate_proj.weight"] = partial(
+                fn, is_column=True)
+            base_actions["layers.0.mlp.up_proj.weight"] = partial(
+                fn, is_column=True)
+
+            for key, action in base_actions.items():
+                if "layers.0." in key:
+                    for i in range(num_layers):
+                        final_actions[key.replace("layers.0.",
+                                                  f"layers.{i}.")] = action
+                final_actions[key] = action
+
+            return final_actions
+
+        mappings = get_tensor_parallel_split_mappings(config.num_layers)
+
+        return mappings