chore: import upstream snapshot with attribution

python/sglang/srt/models/torch_native_llama.py

@@ -0,0 +1,506 @@
+# 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/llama.py#L1
+"""
+Inference-only LLaMA model compatible with HuggingFace weights.
+
+This model supports tensor parallelism (TP) using the PyTorch tensor parallel package.
+Reference: https://pytorch.org/docs/stable/distributed.tensor.parallel.html
+
+Here is a quick example to enable TP:
+```python
+from sglang.srt.layers.model_parallel import tensor_parallel
+
+device_mesh = torch.distributed.init_device_mesh("cuda", (tp_size,))
+tensor_parallel(model, device_mesh)
+```
+
+An end-to-end example can be found in `python/sglang/bench_one_batch.py`.
+You can run it with the following command:
+```bash
+$ python3 -m sglang.bench_one_batch --correct \
+  --model meta-llama/Meta-Llama-3-8B \
+  --json-model-override-args '{"architectures": ["TorchNativeLlamaForCausalLM"]}' \
+  --tensor-parallel-size 2 \
+  --disable-cuda-graph
+```
+We will enable CUDA Graph support soon.
+"""
+
+import types
+from typing import Any, Dict, Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+from torch.nn.parameter import Parameter
+from transformers import LlamaConfig
+
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
+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
+
+tp_size: Optional[int] = None
+tp_rank: Optional[int] = None
+
+
+def gate_up_proj_weight_loader(
+    self,
+    param: Parameter,
+    loaded_weight: torch.Tensor,
+    loaded_shard_id: int,
+):
+    # shard_id: (shard_offset, shard_size)
+    gate_up_offsets = {}
+    current_shard_offset = 0
+    for i, output_size in enumerate(self.output_sizes):
+        # Everything shrinks by tp_size if TP enabled
+        output_size = output_size // tp_size
+        gate_up_offsets[i] = (current_shard_offset, output_size)
+        current_shard_offset += output_size
+    # Re-size the param to the size after TP
+    if current_shard_offset != param.shape[0]:
+        # The clone will free the original, full tensor
+        param.data = param.data.narrow(0, 0, current_shard_offset).clone()
+
+    # Now load gate or up
+    assert loaded_shard_id < len(self.output_sizes)
+    param_data = param.data
+    shard_offset, shard_size = gate_up_offsets[loaded_shard_id]
+    param_data = param_data.narrow(0, shard_offset, shard_size)
+    loaded_weight = loaded_weight.narrow(0, tp_rank * shard_size, shard_size)
+    assert param_data.shape == loaded_weight.shape
+    param_data.copy_(loaded_weight)
+
+
+class LlamaMLP(nn.Module):
+    _tp_plan = {
+        "gate_up_proj": "Colwise_Sharded",
+        "down_proj": "Rowwise",
+    }
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = torch.nn.Linear(
+            hidden_size,
+            intermediate_size * 2,
+            bias=False,
+        )
+        self.gate_up_proj.output_sizes = [intermediate_size] * 2
+        self.gate_up_proj.weight_loader = types.MethodType(
+            gate_up_proj_weight_loader, self.gate_up_proj
+        )
+        self.gate_up_proj.weight.weight_loader = self.gate_up_proj.weight_loader
+        self.down_proj = torch.nn.Linear(intermediate_size, hidden_size, bias=False)
+        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.down_proj(x)
+        return x
+
+
+def qkv_proj_weight_loader(
+    self,
+    param: Parameter,
+    loaded_weight: torch.Tensor,
+    loaded_shard_id: str,
+):
+    num_heads = self.num_heads // tp_size
+    num_kv_heads = self.num_kv_heads // tp_size
+    # shard_id: (shard_offset, shard_size)
+    qkv_offsets = {
+        "q": (0, num_heads * self.head_size),
+        "k": (num_heads * self.head_size, num_kv_heads * self.head_size),
+        "v": (
+            (num_heads + num_kv_heads) * self.head_size,
+            num_kv_heads * self.head_size,
+        ),
+    }
+    total_size = qkv_offsets["v"][0] + qkv_offsets["v"][1]
+    # Re-size the param to the size after TP
+    if total_size != param.shape[0]:
+        # The clone will free the original, full tensor
+        param.data = param.data.narrow(0, 0, total_size).clone()
+
+    # Now load q, k or v
+    shard_offset, shard_size = qkv_offsets[loaded_shard_id]
+    param_data = param.data
+    param_data = param_data.narrow(0, shard_offset, shard_size)
+    loaded_weight = loaded_weight.narrow(0, tp_rank * shard_size, shard_size)
+    assert param_data.shape == loaded_weight.shape
+    param_data.copy_(loaded_weight)
+
+
+class LlamaAttention(nn.Module):
+    _tp_plan = {
+        "qkv_proj": "Colwise_Sharded",
+        "o_proj": "Rowwise",
+    }
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        layer_id: int = 0,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        rope_is_neox_style: bool = True,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
+        self.head_dim = getattr(
+            config, "head_dim", self.hidden_size // self.total_num_heads
+        )
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = torch.nn.Linear(
+            hidden_size,
+            (self.total_num_heads + 2 * self.total_num_kv_heads) * self.head_dim,
+            bias=False,
+        )
+        self.qkv_proj.head_size = self.head_dim
+        self.qkv_proj.num_heads = self.total_num_heads
+        self.qkv_proj.num_kv_heads = self.total_num_kv_heads
+        self.qkv_proj.weight_loader = types.MethodType(
+            qkv_proj_weight_loader, self.qkv_proj
+        )
+        self.qkv_proj.weight.weight_loader = self.qkv_proj.weight_loader
+        self.qkv_proj.weight.output_dim = 0
+        self.o_proj = torch.nn.Linear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+        )
+        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,
+            is_neox_style=rope_is_neox_style,
+        )
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            layer_id=layer_id,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        qkv = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v, forward_batch)
+        output = self.o_proj(attn_output)
+        return output
+
+
+class LlamaDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: LlamaConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = config.rope_parameters["rope_theta"]
+        rope_scaling = config.rope_parameters
+        if rope_scaling is not None and getattr(
+            config, "original_max_position_embeddings", None
+        ):
+            rope_scaling["original_max_position_embeddings"] = (
+                config.original_max_position_embeddings
+            )
+        rope_is_neox_style = getattr(config, "rope_is_neox_style", True)
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
+        self.self_attn = LlamaAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            layer_id=layer_id,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            rope_is_neox_style=rope_is_neox_style,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            prefix=add_prefix("self_attn", prefix),
+        )
+        self.mlp = LlamaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            prefix=add_prefix("mlp", prefix),
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        residual: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # 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(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class LlamaModel(nn.Module):
+    def __init__(
+        self,
+        config: LlamaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+
+        global tp_size, tp_rank
+        if tp_size is None:
+            tp_size = get_parallel().tp_size
+        if tp_rank is None:
+            tp_rank = get_parallel().tp_rank
+
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList(
+            [
+                LlamaDecoderLayer(
+                    config, i, quant_config=quant_config, prefix=f"model.layers.{i}"
+                )
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        if input_embeds is None:
+            hidden_states = self.embed_tokens(input_ids)
+        else:
+            hidden_states = input_embeds
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                forward_batch,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class TorchNativeLlamaForCausalLM(nn.Module):
+    def __init__(
+        self,
+        config: LlamaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.supports_torch_tp = True
+        self.model = LlamaModel(config, quant_config=quant_config)
+        if self.config.tie_word_embeddings:
+            self.lm_head = self.model.embed_tokens
+        else:
+            self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.logits_processor = LogitsProcessor(config)
+
+        # turning off autotune for fp8dq since it doesn't give speedup and
+        # increases compile time significantly
+        torch._inductor.config.max_autotune_gemm_backends = "ATEN"
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> LogitsProcessorOutput:
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def get_module_name_from_weight_name(self, name):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id, num_shard)
+            ("qkv_proj", "q_proj", "q", 3),
+            ("qkv_proj", "k_proj", "k", 3),
+            ("qkv_proj", "v_proj", "v", 3),
+            ("gate_up_proj", "gate_proj", 0, 2),
+            ("gate_up_proj", "up_proj", 1, 2),
+        ]
+        for param_name, weight_name, shard_id, num_shard in stacked_params_mapping:
+            if weight_name in name:
+                return (
+                    name.replace(weight_name, param_name)[: -len(".weight")],
+                    num_shard,
+                )
+        return name[: -len(".weight")], 1
+
+    def get_num_params(self):
+        params_dict = dict(self.named_parameters())
+        return len(params_dict)
+
+    def load_weights_to_module(
+        self,
+        fqn: str,
+        weights: Iterable[Tuple[str, torch.Tensor]],
+    ):
+        """Load weights onto submodule pointed by path `fqn`."""
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        module = self.get_submodule(fqn)
+        params_dict = dict(module.named_parameters(prefix=fqn, recurse=False))
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name or "projector" in name:
+                continue
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            if name.startswith("model.vision_tower") and name not in params_dict:
+                continue
+            if self.config.tie_word_embeddings and "lm_head.weight" 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") or 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") or name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+    def load_weights(
+        self,
+        weights: Iterable[Tuple[str, torch.Tensor]],
+    ):
+        """Load weights onto the full model."""
+        self.load_weights_to_module("", weights)
+
+
+class TorchNativePhi3ForCausalLM(TorchNativeLlamaForCausalLM):
+    pass
+
+
+EntryClass = [TorchNativeLlamaForCausalLM, TorchNativePhi3ForCausalLM]