chore: import upstream snapshot with attribution

python/sglang/srt/models/persimmon.py

@@ -0,0 +1,331 @@
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+from torch import nn
+from transformers import PersimmonConfig
+
+from sglang.srt.distributed import get_pp_group
+from sglang.srt.layers.activation import get_act_fn
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
+from sglang.srt.layers.quantization import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.utils import PPMissingLayer
+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, make_layers
+
+
+class PersimmonMLP(nn.Module):
+
+    def __init__(
+        self, config: PersimmonConfig, quant_config: Optional[QuantizationConfig] = None
+    ):
+        super().__init__()
+        self.dense_h_to_4h = ColumnParallelLinear(
+            config.hidden_size, config.intermediate_size, quant_config=quant_config
+        )
+        self.dense_4h_to_h = RowParallelLinear(
+            config.intermediate_size, config.hidden_size, quant_config=quant_config
+        )
+        self.act = get_act_fn(config.hidden_act)
+
+    def forward(self, hidden_states) -> torch.Tensor:
+        hidden_states, _ = self.dense_h_to_4h(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.dense_4h_to_h(hidden_states)
+        return hidden_states
+
+
+class PersimmonAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: PersimmonConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        layer_id: int = 0,
+    ):
+        super().__init__()
+        self.config = config
+        tensor_parallel_world_size = get_parallel().tp_size
+
+        self.hidden_size = config.hidden_size
+        self.total_num_heads = config.num_attention_heads
+        self.num_heads = self.total_num_heads // tensor_parallel_world_size
+        self.head_dim = self.hidden_size // self.total_num_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_parameters["rope_theta"]
+        self.partial_rotary_factor = config.partial_rotary_factor
+        self.is_causal = True
+
+        assert (self.head_dim * self.total_num_heads) == self.hidden_size
+        assert self.total_num_heads % tensor_parallel_world_size == 0
+
+        self.query_key_value = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.dense = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.is_qk_layernorm = config.qk_layernorm
+
+        if self.is_qk_layernorm:
+            self.q_layernorm = nn.LayerNorm(self.head_dim)
+            self.k_layernorm = nn.LayerNorm(self.head_dim)
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=self.rope_theta,
+            partial_rotary_factor=self.partial_rotary_factor,
+        )
+        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 _split_heads(self, x: torch.Tensor) -> torch.Tensor:
+        seq_length = x.shape[0]
+        return x.view(seq_length, self.num_heads, self.head_dim)
+
+    def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
+        seq_length = x.shape[0]
+        return x.view(seq_length, self.num_heads * self.head_dim)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        forward_batch: ForwardBatch,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.query_key_value(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+
+        if self.is_qk_layernorm:
+            q = self._split_heads(q)
+            k = self._split_heads(k)
+
+            q = self.q_layernorm(q)
+            k = self.k_layernorm(k)
+
+            q = self._merge_heads(q)
+            k = self._merge_heads(k)
+
+        q, k = self.rotary_emb(position_ids, q, k)
+        attn_output = self.attn(q, k, v, forward_batch=forward_batch)
+        output, _ = self.dense(attn_output)
+        return output
+
+
+class PersimmonDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PersimmonConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        idx: int = 0,
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = PersimmonAttention(
+            config=config,
+            quant_config=quant_config,
+            prefix=add_prefix("self_attn", prefix),
+            layer_id=idx,
+        )
+        self.mlp = PersimmonMLP(config, quant_config=quant_config)
+        self.input_layernorm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps
+        )
+        self.post_attention_layernorm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps
+        )
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        forward_batch: ForwardBatch,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states = self.self_attn(
+            position_ids=position_ids,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+
+        hidden_states = hidden_states + residual
+
+        outputs = hidden_states
+        return outputs
+
+
+class PersimmonModel(nn.Module):
+
+    def __init__(
+        self,
+        config: PersimmonConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.pp_group = get_pp_group()
+
+        if self.pp_group.is_first_rank:
+            self.embed_tokens = VocabParallelEmbedding(
+                config.vocab_size, config.hidden_size
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.layers, self.start_layer, self.end_layer = make_layers(
+            config.num_hidden_layers,
+            lambda idx, prefix: PersimmonDecoderLayer(
+                config, quant_config=quant_config, prefix=prefix, idx=idx
+            ),
+            prefix="model.layers",
+            pp_rank=self.pp_group.rank_in_group,
+            pp_size=self.pp_group.world_size,
+        )
+
+        if self.pp_group.is_last_rank:
+            self.final_layernorm = nn.LayerNorm(
+                config.hidden_size, eps=config.layer_norm_eps
+            )
+        else:
+            self.final_layernorm = PPMissingLayer()
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        forward_batch: ForwardBatch,
+        positions: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if self.pp_group.is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+        else:
+            hidden_states = forward_batch.pp_input_hidden
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            hidden_states = layer(
+                position_ids=positions,
+                forward_batch=forward_batch,
+                hidden_states=hidden_states,
+            )
+        return self.final_layernorm(hidden_states)
+
+
+class PersimmonForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: PersimmonConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = PersimmonModel(
+            config=config, quant_config=quant_config, prefix=add_prefix("model", prefix)
+        )
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.logits_processor = LogitsProcessor(config)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> LogitsProcessorOutput:
+        hidden_states = self.model(
+            input_ids=input_ids,
+            forward_batch=forward_batch,
+            positions=positions,
+            inputs_embeds=inputs_embeds,
+        )
+
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if name not in params_dict:
+                if name == "lm_head.weight":
+                    continue
+                print(f"Warning: weight {name} not found in model.")
+                continue
+            param = params_dict[name]
+            if "query_key_value" in name:
+                output_dim = getattr(param, "output_dim", None)
+                if output_dim is not None:
+                    loaded_weight_shape = loaded_weight.shape
+                    num_heads = self.config.num_attention_heads
+                    loaded_weight = loaded_weight.view(
+                        loaded_weight_shape[:output_dim]
+                        + (num_heads, 3, -1)
+                        + loaded_weight_shape[output_dim + 1 :]
+                    )
+                    loaded_weight = loaded_weight.transpose(output_dim, output_dim + 1)
+                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+EntryClass = PersimmonForCausalLM