Add support for IBM Granite 3.x models (#2437)

docs/references/supported_models.md

@@ -29,6 +29,7 @@
 - SmolLM
 - GLM-4
 - Phi-3-Small
+- IBM Granite 3
 
 ## Embedding Models
 

python/sglang/lang/chat_template.py

@@ -320,6 +320,28 @@ register_chat_template(
     )
 )
 
+register_chat_template(
+    ChatTemplate(
+        name="granite-3-instruct",
+        default_system_prompt=None,
+        role_prefix_and_suffix={
+            "system": (
+                "<|start_of_role|>system<|end_of_role|>",
+                "<|end_of_text|>",
+            ),
+            "user": (
+                "<|start_of_role|>user<|end_of_role|>",
+                "<|end_of_text|>",
+            ),
+            "assistant": (
+                "<|start_of_role|>assistant<|end_of_role|>",
+                "<|end_of_text|>",
+            ),
+        },
+        stop_str=("<|end_of_text|>",),
+    )
+)
+
 
 @register_chat_template_matching_function
 def match_dbrx(model_path: str):
@@ -402,6 +424,16 @@ def match_c4ai_command_r(model_path: str):
         return get_chat_template("c4ai-command-r")
 
 
+@register_chat_template_matching_function
+def match_granite_instruct(model_path: str):
+    model_path = model_path.lower()
+    # When future versions of Granite are released, this code may
+    # need to be updated. For now, assume that the Granite 3.0
+    # template works across the board.
+    if "granite" in model_path and "instruct" in model_path:
+        return get_chat_template("granite-3-instruct")
+
+
 if __name__ == "__main__":
     messages = [
         {"role": "system", "content": None},  # None means default

python/sglang/srt/layers/logits_processor.py

@@ -91,9 +91,12 @@ class LogitsMetadata:
 
 
 class LogitsProcessor(nn.Module):
-    def __init__(self, config, skip_all_gather: bool = False):
+    def __init__(
+        self, config, skip_all_gather: bool = False, logit_scale: Optional[float] = None
+    ):
         super().__init__()
         self.config = config
+        self.logit_scale = logit_scale
         self.do_tensor_parallel_all_gather = (
             not skip_all_gather and get_tensor_model_parallel_world_size() > 1
         )
@@ -240,6 +243,9 @@ class LogitsProcessor(nn.Module):
                 all_logits = self._get_logits(states, lm_head)
                 if self.do_tensor_parallel_all_gather:
                     all_logits = tensor_model_parallel_all_gather(all_logits)
+
+                # The LM head's weights may be zero-padded for parallelism. Remove any
+                # extra logits that this padding may have produced.
                 all_logits = all_logits[:, : self.config.vocab_size].float()
 
                 if hasattr(self.config, "final_logit_softcapping"):
@@ -302,6 +308,10 @@ class LogitsProcessor(nn.Module):
         else:
             # GGUF models
             logits = lm_head.linear_method.apply(lm_head, hidden_states, embedding_bias)
+
+        # Optional scaling factor, backported from vLLM 0.4
+        if self.logit_scale is not None:
+            logits.mul_(self.logit_scale)  # In-place multiply
         return logits
 
 

python/sglang/srt/models/granite.py

@@ -0,0 +1,517 @@
+# 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 Granite model compatible with HuggingFace weights."""
+
+import logging
+from typing import Any, Dict, Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import GraniteConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.rotary_embedding import get_rope
+
+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, 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.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.utils import get_exception_traceback
+
+logger = logging.getLogger(__name__)
+
+
+class GraniteMLP(nn.Module):
+    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 = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+        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
+
+
+class GraniteAttention(nn.Module):
+    def __init__(
+        self,
+        config: GraniteConfig,
+        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
+        tp_size = get_tensor_model_parallel_world_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 = config.attention_multiplier
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        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 GraniteDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: GraniteConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.residual_multiplier = config.residual_multiplier
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        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 = GraniteAttention(
+            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=f"{prefix}.self_attn",
+        )
+        self.mlp = GraniteMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+        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,
+            )
+            * self.residual_multiplier
+        )  # multiplier for Maximal Update Parameterization
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states) * self.residual_multiplier
+        return hidden_states, residual
+
+
+class GraniteModel(nn.Module):
+    def __init__(
+        self,
+        config: GraniteConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        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(
+            [
+                GraniteDecoderLayer(
+                    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
+        hidden_states *= self.config.embedding_multiplier
+        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 GraniteForCausalLM(nn.Module):
+    def __init__(
+        self,
+        config: GraniteConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = GraniteModel(config, quant_config=quant_config)
+        # If tie_word_embeddings == True, then input and output embeddings are
+        # the same tensor. Enforce during object creation so that weights will
+        # load correctly even if the LM head weights don't have a separate entry
+        # in the state dict.
+        self.lm_head = ParallelLMHead(
+            config.vocab_size, config.hidden_size, quant_config=quant_config
+        )
+        if self.config.tie_word_embeddings:
+            self.lm_head.tie_weights(self.model.embed_tokens)
+
+        # Granite logit scaling factors are applied via division, but
+        # LogitsProcessor expects a multiplicative factor.
+        if hasattr(config, "logits_scaling"):
+            logit_scale = 1.0 / config.logits_scaling
+        else:
+            logit_scale = None
+        self.logits_processor = LogitsProcessor(config, logit_scale=logit_scale)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+        self.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),
+        ]
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        get_embedding: bool = False,
+    ) -> LogitsProcessorOutput:
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        if not get_embedding:
+            logits_processor_output: LogitsProcessorOutput = self.logits_processor(
+                input_ids, hidden_states, self.lm_head, forward_batch
+            )
+            return logits_processor_output
+        else:
+            return self.pooler(hidden_states, forward_batch)
+
+    def get_hidden_dim(self, module_name):
+        # return input_dim, output_dim
+        if module_name in ["q_proj", "o_proj", "qkv_proj"]:
+            return self.config.hidden_size, self.config.hidden_size
+        elif module_name in ["kv_proj"]:
+            return self.config.hidden_size, self.config.hidden_size // (
+                self.config.num_attention_heads // self.config.num_key_value_heads
+            )
+        elif module_name == "gate_up_proj":
+            return self.config.hidden_size, self.config.intermediate_size
+        elif module_name == "down_proj":
+            return self.config.intermediate_size, self.config.hidden_size
+        else:
+            raise NotImplementedError()
+
+    def get_module_name(self, name):
+        params_mapping = {
+            "q_proj": "qkv_proj",
+            "k_proj": "qkv_proj",
+            "v_proj": "qkv_proj",
+            "gate_proj": "gate_up_proj",
+            "up_proj": "gate_up_proj",
+        }
+        return params_mapping.get(name, name)
+
+    def get_module_name_from_weight_name(self, name):
+        for param_name, weight_name, shard_id, num_shard in self.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(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"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+
+        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 "lm_head.weight" in name and self.config.tie_word_embeddings:
+                # Input and output embeddings are tied, so the output embeddings
+                # may not be present in the checkpoint. We assume that the input
+                # embeddings are always present in the checkpoint.
+                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:
+                # This block only runs if the preceding for loop doesn't find
+                # a match for `name` in `stacked_params_mapping`.
+
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip loading kv_scale from ckpts towards new design.
+                if name.endswith(".kv_scale") 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)
+
+    def get_weights_by_name(
+        self, name: str, truncate_size: int = 100, tp_size: int = 1
+    ) -> Optional[torch.Tensor]:
+        """Get the weights of the parameter by its name. Similar to `get_parameter` in Hugging Face.
+
+        Only used for unit test with an unoptimized performance.
+        For optimized performance, please use torch.save and torch.load.
+        """
+        try:
+            if name == "lm_head.weight" and self.config.tie_word_embeddings:
+                logger.info(
+                    "word embedding is tied for this model, return embed_tokens.weight as lm_head.weight."
+                )
+                return (
+                    self.model.embed_tokens.weight.cpu()
+                    .to(torch.float32)
+                    .numpy()
+                    .tolist()[:truncate_size]
+                )
+
+            mapped_name = name
+            mapped_shard_id = None
+            for param_name, weight_name, shard_id in self.stacked_params_mapping:
+                if weight_name in name:
+                    mapped_name = name.replace(weight_name, param_name)
+                    mapped_shard_id = shard_id
+                    break
+            params_dict = dict(self.named_parameters())
+            param = params_dict[mapped_name]
+            if mapped_shard_id is not None:
+                if mapped_shard_id in ["q", "k", "v"]:
+                    num_heads = self.config.num_attention_heads // tp_size
+                    num_kv_heads = self.config.num_key_value_heads // tp_size
+                    head_dim = (
+                        self.config.hidden_size // self.config.num_attention_heads
+                    )
+                    if mapped_shard_id == "q":
+                        offset = 0
+                        size = num_heads * head_dim
+                    elif mapped_shard_id == "k":
+                        offset = num_heads * head_dim
+                        size = num_kv_heads * head_dim
+                    elif mapped_shard_id == "v":
+                        offset = (num_heads + num_kv_heads) * head_dim
+                        size = num_kv_heads * head_dim
+                    weight = param.data.narrow(0, offset, size)
+                elif mapped_shard_id in [0, 1]:
+                    intermediate_size = self.config.intermediate_size
+                    slice_size = intermediate_size // tp_size
+                    if mapped_shard_id == 0:  # gate_proj
+                        offset = 0
+                        size = slice_size
+                    elif mapped_shard_id == 1:  # up_proj
+                        offset = slice_size
+                        size = slice_size
+
+                    weight = param.data.narrow(0, offset, size)
+                else:
+                    weight = param.data
+            else:
+                weight = param.data
+            if tp_size > 1 and ("o_proj" in name or "down_proj" in name):
+                gathered_weights = [torch.zeros_like(weight) for _ in range(tp_size)]
+                torch.distributed.all_gather(gathered_weights, weight)
+                weight = torch.cat(gathered_weights, dim=1)
+            return weight.cpu().to(torch.float32).numpy().tolist()[:truncate_size]
+
+        except Exception:
+            logger.error(
+                f"Error getting weights by name {name} in GraniteForCausalLM: {get_exception_traceback()}"
+            )
+            return None
+
+
+EntryClass = [GraniteForCausalLM]

test/srt/models/test_generation_models.py

@@ -57,6 +57,7 @@ ALL_OTHER_MODELS = [
     ModelCase("openai-community/gpt2"),
     ModelCase("microsoft/Phi-3-small-8k-instruct"),
     ModelCase("allenai/OLMo-2-1124-7B-Instruct", skip_long_prompt=True),
+    ModelCase("ibm-granite/granite-3.0-2b-instruct", skip_long_prompt=True),
 ]
 
 TORCH_DTYPES = [torch.float16]