chore: import upstream snapshot with attribution

python/sglang/srt/models/qwen.py

@@ -0,0 +1,357 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2023-2024 SGLang Team
+# 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.
+# ==============================================================================
+
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/qwen.py#L1
+
+from typing import Any, Dict, Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.runtime_context import get_parallel
+from sglang.srt.utils import add_prefix
+from sglang.srt.utils.hf_transformers_utils import get_rope_config
+
+
+class QWenMLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str = "silu",
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            2 * [intermediate_size],
+            bias=False,
+            gather_output=False,
+            quant_config=quant_config,
+            prefix=add_prefix("gate_up_proj", prefix),
+        )
+        self.c_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            quant_config=quant_config,
+            prefix=add_prefix("c_proj", prefix),
+        )
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. "
+                "Only silu is supported for now."
+            )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.c_proj(x)
+        return x
+
+
+class QWenAttention(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        max_position_embeddings: int,
+        layer_id: int = 0,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        tensor_model_parallel_world_size = get_parallel().tp_size
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
+        self.head_dim = hidden_size // self.total_num_heads
+
+        # pylint: disable=invalid-name
+        self.c_attn = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=add_prefix("c_attn", prefix),
+        )
+        self.c_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            quant_config=quant_config,
+            prefix=add_prefix("c_proj", prefix),
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.scaling = self.head_dim**-0.5
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_heads,
+            layer_id=layer_id,
+            quant_config=quant_config,
+            prefix=add_prefix("attn", prefix),
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        qkv, _ = self.c_attn(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v, forward_batch)
+        output, _ = self.c_proj(attn_output)
+        return output
+
+
+class QWenBlock(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.ln_1 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+        rope_theta, rope_scaling = get_rope_config(config)
+        self.attn = QWenAttention(
+            config.hidden_size,
+            config.num_attention_heads,
+            config.max_position_embeddings,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            layer_id=layer_id,
+            quant_config=quant_config,
+            prefix=add_prefix("attn", prefix),
+        )
+
+        self.ln_2 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+        self.mlp = QWenMLP(
+            config.hidden_size,
+            config.intermediate_size // 2,
+            quant_config=quant_config,
+            prefix=add_prefix("mlp", prefix),
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        hidden_states = self.attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.ln_2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class QWenModel(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.vocab_size = config.vocab_size
+
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.wte = VocabParallelEmbedding(
+            vocab_size,
+            config.hidden_size,
+            prefix=add_prefix("wte", prefix),
+        )
+        self.h = nn.ModuleList(
+            [
+                QWenBlock(
+                    config,
+                    i,
+                    quant_config=quant_config,
+                    prefix=add_prefix(f"h.{i}", prefix),
+                )
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+        self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        hidden_states = self.wte(input_ids)
+        for i in range(len(self.h)):
+            layer = self.h[i]
+            hidden_states = layer(
+                positions,
+                hidden_states,
+                forward_batch,
+            )
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class QWenLMHeadModel(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.transformer = QWenModel(
+            config, quant_config=quant_config, prefix=add_prefix("transformer", prefix)
+        )
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.lm_head = ParallelLMHead(
+            vocab_size, config.hidden_size, prefix=add_prefix("lm_head", prefix)
+        )
+        self.logits_processor = LogitsProcessor(config)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ):
+        hidden_states = self.transformer(input_ids, positions, forward_batch)
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    @torch.no_grad()
+    def forward_split_prefill(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        split_interval: Tuple[int, int],  # [start, end) 0-based
+    ):
+        start, end = split_interval
+        # embed
+        if start == 0:
+            forward_batch.hidden_states = self.transformer.wte(input_ids)
+
+        # decoder layer
+        for i in range(start, end):
+            layer = self.transformer.h[i]
+            forward_batch.hidden_states = layer(
+                positions,
+                forward_batch.hidden_states,
+                forward_batch,
+            )
+
+        if end == self.transformer.config.num_hidden_layers:
+            # norm
+            forward_batch.hidden_states = self.transformer.ln_f(
+                forward_batch.hidden_states
+            )
+            # logits process
+            result = self.logits_processor(
+                input_ids, forward_batch.hidden_states, self.lm_head, forward_batch
+            )
+        else:
+            result = None
+
+        return result
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("gate_up_proj", "w2", 0),
+            ("gate_up_proj", "w1", 1),
+        ]
+        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
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+EntryClass = QWenLMHeadModel