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vllm/model_executor/models/phi.py

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+# coding=utf-8
+# Adapted from
+# https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_phi.py
+# Copyright 2023 The vLLM team.
+# Copyright (c) Microsoft Corporation.
+# Copyright (c) 2024 - 2024 Moore Threads Technology Co., Ltd("Moore Threads"). All rights reserved.
+# Licensed under the MIT license.
+#
+# BSD 3-Clause License
+#
+# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""Inference-only Phi-1.5 model compatible with HuggingFace weights."""
+from typing import Iterable, List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.attention import Attention, AttentionMetadata
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import SamplerOutput
+
+
+class PhiAttention(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+        self.total_num_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_size = self.hidden_size // self.total_num_heads
+
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = (self.total_num_heads //
+                          tensor_model_parallel_world_size)
+
+        # pylint: disable=C0103
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_size,
+            self.total_num_heads,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.dense = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            quant_config=quant_config,
+        )
+
+        scaling = self.head_size**-0.5
+        rotary_dim = int(config.partial_rotary_factor *
+                         (config.hidden_size // config.num_attention_heads))
+        assert rotary_dim % 2 == 0
+
+        # pylint: disable=C0301
+        # Refer to:
+        # https://huggingface.co/microsoft/phi-1_5/blob/d212a789620c380ff32ca1d1ee9943a777360987/modeling_phi.py#L518
+        rope_theta = 10000
+        max_position_embeddings = getattr(config, "n_positions", 2048)
+        self.rotary_emb = get_rope(
+            self.head_size,
+            rotary_dim=rotary_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+        )
+        self.attn = Attention(self.num_heads, self.head_size, scaling)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k = self.rotary_emb(position_ids, q, k)
+        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        output, _ = self.dense(attn_output)
+        return output
+
+
+class PhiMLP(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+
+        n_inner = getattr(config, "n_inner", None)
+        n_inner = n_inner if n_inner is not None else 4 * config.hidden_size
+
+        self.fc1 = ColumnParallelLinear(
+            config.hidden_size,
+            n_inner,
+            quant_config=quant_config,
+        )
+        self.fc2 = RowParallelLinear(
+            n_inner,
+            config.hidden_size,
+            quant_config=quant_config,
+        )
+        self.act = get_act_fn(config.hidden_act, quant_config, n_inner)
+
+    def forward(self, hidden_states):
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        return hidden_states
+
+
+class PhiLayer(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.layer_norm_eps)
+        self.self_attn = PhiAttention(config, quant_config)
+        self.mlp = PhiMLP(config, quant_config)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        attn_outputs = self.self_attn(
+            position_ids=position_ids,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            attn_metadata=attn_metadata,
+        )
+        feed_forward_hidden_states = self.mlp(hidden_states)
+        hidden_states = attn_outputs + feed_forward_hidden_states + residual
+        return hidden_states
+
+
+class PhiModel(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
+                                                   config.hidden_size)
+        self.layers = nn.ModuleList([
+            PhiLayer(config, quant_config)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.final_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        for i in range(self.config.num_hidden_layers):
+            layer = self.layers[i]
+            hidden_states = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                attn_metadata,
+            )
+
+        hidden_states = self.final_layernorm(hidden_states)
+
+        return hidden_states
+
+
+class PhiForCausalLM(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+
+        self.model = PhiModel(config, quant_config)
+
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      bias=True)
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.sampler = Sampler()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   attn_metadata)
+
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head.weight, hidden_states,
+                                       sampling_metadata, self.lm_head.bias)
+        return logits
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v")
+        ]
+        params_dict = dict(self.named_parameters())
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # pylint: disable=E1136
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)