add phi-3 small support (#2062)

Co-authored-by: Tushar Goel <114812108+AI-Tushar@users.noreply.github.com>
2024-11-17 18:47:43 -08:00
parent 9c745d078e
commit 8c280cee55
3 changed files with 449 additions and 0 deletions
--- a/python/sglang/srt/models/phi3_small.py
+++ b/python/sglang/srt/models/phi3_small.py
@@ -0,0 +1,447 @@
+import math
+from typing import Dict, Iterable, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from transformers import Phi3Config
+from transformers.configuration_utils import PretrainedConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.utils import make_layers, maybe_prefix
+
+from sglang.srt.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
+from sglang.srt.layers.pooler import Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.torchao_utils import apply_torchao_config_
+from sglang.srt.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE,
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.managers.schedule_batch import global_server_args_dict
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+
+
+@torch.jit.script
+def quick_gelu(x):
+    return x * torch.sigmoid(1.702 * x)
+
+
+@torch.jit.script
+def gegelu(input, limit: Optional[float] = None):
+    a_gelu, a_linear = input[..., ::2], input[..., 1::2]
+    if limit is not None:
+        a_gelu = torch.where(
+            torch.isinf(a_gelu), a_gelu, a_gelu.clamp(min=None, max=limit)
+        )
+        a_linear = torch.where(
+            torch.isinf(a_linear),
+            a_linear,
+            a_linear.clamp(min=-limit, max=limit),
+        )
+    out_gelu = quick_gelu(a_gelu)
+    return out_gelu * (a_linear + 1)
+
+
+class Phi3SmallMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        assert (
+            self.config.hidden_act == "gegelu"
+        ), "Only `gegelu` is supported for the 4.7 series of models .."
+        self.hidden_size = config.hidden_size
+        self.gegelu_limit = config.gegelu_limit
+        self.intermediate_size = config.intermediate_size
+
+        self.up_proj = MergedColumnParallelLinear(
+            self.hidden_size,
+            2 * [self.intermediate_size],
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            self.intermediate_size,
+            self.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+        )
+
+    def forward(self, x):
+        gate_up, _ = self.up_proj(x)
+        x = gegelu(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Phi3SmallSelfAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.layer_id = layer_id
+        self.config = config
+        self.sparse_block_size = config.blocksparse_block_size
+        self.homo_heads = config.blocksparse_homo_head_pattern
+        self.local_blocks = config.blocksparse_num_local_blocks
+        self.vert_stride = config.blocksparse_vert_stride
+
+        assert (
+            config.blocksparse_block_size == config.blocksparse_triton_kernel_block_size
+        )
+
+        self.hidden_size = config.hidden_size
+        # Number of Query Heads
+        self.num_heads = config.num_attention_heads
+
+        self.head_dim = self.hidden_size // self.num_heads
+        self.tp_size = get_tensor_model_parallel_world_size()
+        # Number of total Key Value Heads before tensor parallel
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_q_per_kv = self.num_heads // self.num_key_value_heads
+        if self.tp_size > 1:
+            assert self.num_key_value_heads % self.tp_size == 0
+        self.num_kv_heads_per_partion = max(1, self.num_key_value_heads // self.tp_size)
+        self.num_heads_per_partition = self.num_heads // self.tp_size
+
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_embedding_base = config.rope_embedding_base
+        self.rope_position_scale = config.rope_position_scale
+        self.is_causal = True
+
+        norm_factor = None
+        if config.mup_use_scaling:
+            norm_factor = self.head_dim / config.mup_attn_multiplier
+        else:
+            norm_factor = math.sqrt(self.head_dim)
+        self.scale = 1 / norm_factor
+
+        self.query_key_value = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.num_heads,
+            self.num_key_value_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.dense = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        if getattr(self.config, "rope_scaling", None) is not None:
+            rope_scaling = self.config.rope_scaling
+            for key in rope_scaling:
+                if isinstance(rope_scaling[key], list):
+                    rope_scaling[key] = tuple(rope_scaling[key])
+
+            if "factor" not in rope_scaling:
+                rope_scaling["factor"] = self.rope_position_scale
+        else:
+            rope_scaling = {
+                "rope_type": "linear",
+                "factor": self.rope_position_scale,
+            }
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=self.rope_embedding_base,
+            rope_scaling=rope_scaling,
+        )
+
+        # blocksparse params
+        self.blocksparse_block_size = config.blocksparse_block_size
+        self.blocksparse_num_local_blocks = config.blocksparse_num_local_blocks
+        self.blocksparse_vert_stride = config.blocksparse_vert_stride
+
+        use_dense_attn = (
+            getattr(self.config, "dense_attention_every_n_layers", None)
+            and (self.layer_id + 1) % self.config.dense_attention_every_n_layers == 0
+        )
+
+        bs_params = None
+        if not use_dense_attn:
+            bs_params = {
+                "max_seqlen": self.max_position_embeddings,
+                "num_heads": self.num_heads_per_partition,
+                "num_kv_heads": self.num_kv_heads_per_partion,
+                "block_size": self.sparse_block_size,
+                "local_blocks": self.local_blocks,
+                "vert_stride": self.vert_stride,
+                "homo_head": self.homo_heads,
+            }
+
+        self.attn = RadixAttention(
+            self.num_heads_per_partition,
+            self.head_dim,
+            self.scale,
+            num_kv_heads=self.num_kv_heads_per_partion,
+            layer_id=layer_id,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        qkv, _ = self.query_key_value(hidden_states)
+
+        qkv = qkv.view(qkv.shape[:-1] + (-1, (self.num_q_per_kv + 2), self.head_dim))
+        q, k, v = qkv.split([self.num_q_per_kv, 1, 1], dim=-2)
+
+        # NOTE: this is required by RotaryEmbed, which indeed does not have to
+        # TODO: allow 3D QK for rotary forward
+        q = q.reshape(-1, self.head_dim * self.num_heads_per_partition)
+        k = k.reshape(-1, self.head_dim * self.num_kv_heads_per_partion)
+        v = v.reshape(-1, self.head_dim * self.num_kv_heads_per_partion)
+
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v, forward_batch=forward_batch)
+        output, _ = self.dense(attn_output)
+
+        return output
+
+
+class Phi3SmallDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int,
+        cache_config=None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Phi3SmallSelfAttention(
+            config, layer_id, quant_config=quant_config
+        )
+        self.mlp = Phi3SmallMLP(config, quant_config)
+
+        self.input_layernorm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_epsilon
+        )
+        self.post_attention_layernorm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_epsilon
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            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 = residual + hidden_states
+        return hidden_states
+
+
+class Phi3SmallModel(nn.Module):
+
+    def __init__(
+        self,
+        config: Phi3Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.config = config
+        cache_config = None
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size, config.hidden_size
+        )
+        self.mup_embedding_multiplier = config.mup_embedding_multiplier
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Phi3SmallDecoderLayer(
+                config, int(prefix.split(".")[-1]), cache_config, quant_config
+            ),
+            prefix=f"{prefix}.layers",
+        )
+
+        self.final_layernorm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_epsilon
+        )
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        positions: Optional[torch.LongTensor],
+        forward_batch: ForwardBatch,
+        inputs_embeds: Optional[torch.Tensor],
+    ) -> Union[torch.Tensor]:
+
+        if inputs_embeds is not None:
+            hidden_states = inputs_embeds
+        else:
+            hidden_states = self.get_input_embeddings(input_ids)
+        if (
+            self.mup_embedding_multiplier is not None
+            and self.mup_embedding_multiplier > 0.0
+        ):
+            hidden_states = hidden_states * self.mup_embedding_multiplier
+
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            hidden_states = layer(positions, hidden_states, forward_batch=forward_batch)
+
+        hidden_states = self.final_layernorm(hidden_states)
+        return hidden_states
+
+
+class Phi3SmallForCausalLM(nn.Module):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(
+        self,
+        config: Phi3Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+
+        super().__init__()
+
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Phi3SmallModel(
+            config=config,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "model"),
+        )
+        self.torchao_config = global_server_args_dict["torchao_config"]
+        self.vocab_size = config.vocab_size
+        self.mup_width_multiplier = config.mup_width_multiplier
+        self.lm_head = ParallelLMHead(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+            quant_config=quant_config,
+        )
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(config)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+
+        # tokens in tiktoken but not used
+        if hasattr(config, "dummy_token_indices"):
+            device = self.lm_head.weight.device
+            self.register_buffer(
+                "dummy_token_indices",
+                torch.LongTensor(config.dummy_token_indices).to(device),
+                persistent=False,
+            )
+        else:
+            self.dummy_token_indices = None
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, value):
+        self.lm_head = value
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states, sampling_metadata)
+        if self.dummy_token_indices is not None and logits is not None:
+            logits.index_fill_(-1, self.dummy_token_indices, -torch.inf)
+        return logits
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        positions: Optional[torch.LongTensor],
+        forward_batch: ForwardBatch,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        get_embedding: bool = False,
+    ) -> LogitsProcessorOutput:
+        hidden_states = self.model(
+            input_ids=input_ids,
+            positions=positions,
+            forward_batch=forward_batch,
+            inputs_embeds=inputs_embeds,
+        )
+
+        if not get_embedding:
+            return self.logits_processor(
+                input_ids, hidden_states, self.lm_head.weight, forward_batch
+            )
+
+        else:
+            return self.pooler(hidden_states, 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.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)
+
+        apply_torchao_config_(self, params_dict, set(["proj.weight"]))
+
+
+EntryClass = Phi3SmallForCausalLM