Sync from v0.13

vllm/model_executor/models/olmo.py

@@ -1,4 +1,6 @@
-# coding=utf-8
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
 # Adapted from
 # https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/models/olmo/modeling_olmo.py
 # Copyright 2024 The vLLM team.
@@ -21,57 +23,70 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only OLMo model compatible with HuggingFace weights."""
-from typing import Iterable, List, Optional, Tuple
+
+from collections.abc import Iterable
+from itertools import islice
 
 import torch
 from torch import nn
 from transformers import OlmoConfig
 
-from vllm.attention import Attention, AttentionMetadata
-from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.attention.layer import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
-from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
-    ParallelLMHead, VocabParallelEmbedding)
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.sequence import SamplerOutput
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
 
 
 class OlmoAttention(nn.Module):
     """
     This is the attention block where the output is computed as
-    ``Attention(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
+    `Attention(LN(x))` in `MLP(LN(x + Attention(LN(x))))`
     (plus another skip connection).
     """
 
     def __init__(
         self,
         config: OlmoConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         self.config = config
         self.hidden_size = config.hidden_size
-        tensor_model_parallel_world_size = (
-            get_tensor_model_parallel_world_size())
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
         self.total_num_heads = config.num_attention_heads
 
         assert self.hidden_size % self.total_num_heads == 0
         assert self.total_num_heads % tensor_model_parallel_world_size == 0
 
-        self.num_heads = (self.total_num_heads //
-                          tensor_model_parallel_world_size)
+        self.num_heads = self.total_num_heads // tensor_model_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_theta
         self.clip_qkv = config.clip_qkv
 
         # Attention input projection. Projects x -> (q, k, v)
@@ -81,19 +96,24 @@ class OlmoAttention(nn.Module):
             self.total_num_heads,
             bias=config.attention_bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
         )
 
         # Rotary embeddings.
         self.rotary_emb = get_rope(
             self.head_dim,
-            rotary_dim=self.head_dim,
             max_position=self.max_position_embeddings,
-            base=self.rope_theta,
+            rope_parameters=config.rope_parameters,
         )
         self.scaling = self.head_dim**-0.5
-        self.attn = Attention(self.num_heads,
-                              self.head_dim,
-                              scale=self.scaling)
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            scale=self.scaling,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
 
         # Attention output projection.
         self.o_proj = RowParallelLinear(
@@ -101,21 +121,20 @@ class OlmoAttention(nn.Module):
             self.hidden_size,
             bias=config.attention_bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
         )
 
     def forward(
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
         if self.clip_qkv is not None:
             qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
         q, k, v = qkv.chunk(chunks=3, dim=-1)
         q, k = self.rotary_emb(positions, q, k)
-        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        attn_output = self.attn(q, k, v)
         output, _ = self.o_proj(attn_output)
         return output
 
@@ -123,14 +142,15 @@ class OlmoAttention(nn.Module):
 class OlmoMLP(nn.Module):
     """
     This is the MLP block where the output is computed as
-    ``MLP(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
+    `MLP(LN(x))` in `MLP(LN(x + Attention(LN(x))))`
     (plus another skip connection).
     """
 
     def __init__(
         self,
         config: OlmoConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         self.config = config
@@ -143,6 +163,7 @@ class OlmoMLP(nn.Module):
             [self.intermediate_size] * 2,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
         )
 
         # Activation function.
@@ -154,6 +175,7 @@ class OlmoMLP(nn.Module):
             self.hidden_size,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
         )
 
     def forward(
@@ -169,40 +191,43 @@ class OlmoMLP(nn.Module):
 class OlmoDecoderLayer(nn.Module):
     """
     This is a typical transformer block where the output is
-    computed as ``MLP(LN(x + Attention(LN(x))))``
+    computed as `MLP(LN(x + Attention(LN(x))))`
     (plus another skip connection).
     """
 
-    def __init__(self,
-                 config: OlmoConfig,
-                 quant_config: Optional[QuantizationConfig] = None):
+    def __init__(
+        self,
+        config: OlmoConfig,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
         super().__init__()
         # Attention block.
-        self.self_attn = OlmoAttention(config, quant_config)
+        self.self_attn = OlmoAttention(
+            config, cache_config, quant_config, prefix=f"{prefix}.self_attn"
+        )
 
         # MLP block.
-        self.mlp = OlmoMLP(config, quant_config)
+        self.mlp = OlmoMLP(config, quant_config, prefix=f"{prefix}.mlp")
 
         # LayerNorm
-        self.input_layernorm = nn.LayerNorm(config.hidden_size,
-                                            elementwise_affine=False,
-                                            bias=False)
-        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
-                                                     elementwise_affine=False,
-                                                     bias=False)
+        self.input_layernorm = nn.LayerNorm(
+            config.hidden_size, elementwise_affine=False, bias=False
+        )
+        self.post_attention_layernorm = nn.LayerNorm(
+            config.hidden_size, elementwise_affine=False, bias=False
+        )
 
     def forward(
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
-    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, tuple[torch.Tensor, torch.Tensor] | None]:
         # Attention block.
         residual = hidden_states
         hidden_states = self.input_layernorm(hidden_states)
-        hidden_states = self.self_attn(positions, hidden_states, kv_cache,
-                                       attn_metadata)
+        hidden_states = self.self_attn(positions, hidden_states)
         hidden_states = hidden_states + residual
 
         # MLP block.
@@ -213,111 +238,69 @@ class OlmoDecoderLayer(nn.Module):
         return hidden_states
 
 
+@support_torch_compile
 class OlmoModel(nn.Module):
-
-    def __init__(self,
-                 config: OlmoConfig,
-                 quant_config: Optional[QuantizationConfig] = None):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
         self.config = config
 
-        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
-                                                   config.hidden_size)
-        self.layers = nn.ModuleList([
-            OlmoDecoderLayer(config, quant_config)
-            for layer_idx in range(config.num_hidden_layers)
-        ])
-        self.norm = nn.LayerNorm(config.hidden_size,
-                                 elementwise_affine=False,
-                                 bias=False)
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size, config.hidden_size
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: OlmoDecoderLayer(
+                config, cache_config, quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers",
+        )
+        self.norm = nn.LayerNorm(
+            config.hidden_size, elementwise_affine=False, bias=False
+        )
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states"], config.hidden_size
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
 
     def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
+        intermediate_tensors: IntermediateTensors | None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
         """
         :param input_ids: A tensor of shape `(batch_size, seq_len)`.
         """
-        # Get embeddings of input.
-        # shape: (batch_size, seq_len, d_model)
-        inputs_embeds = self.embed_tokens(input_ids)
-
-        # embed positions
-        hidden_states = inputs_embeds
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_input_ids(input_ids)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
 
         # Apply blocks one-by-one.
-        for layer_idx, decoder_layer in enumerate(self.layers):
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
             # shape: (batch_size, seq_len, d_model)
-            hidden_states = decoder_layer(
-                positions,
-                hidden_states,
-                kv_caches[layer_idx],
-                attn_metadata,
-            )
+            hidden_states = layer(positions, hidden_states)
 
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
         # Apply final layer norm.
         # shape: (batch_size, seq_len or 1, d_model)
         hidden_states = self.norm(hidden_states)
         return hidden_states
 
-
-class OlmoForCausalLM(nn.Module):
-    """
-    Extremely barebones HF model wrapper.
-    """
-
-    def __init__(self,
-                 config: OlmoConfig,
-                 quant_config: Optional[QuantizationConfig] = None):
-        super().__init__()
-        self.config = config
-        self.model = OlmoModel(config, quant_config)
-        if config.tie_word_embeddings:
-            self.lm_head_weight = self.model.embed_tokens.weight
-        else:
-            self.unpadded_vocab_size = config.vocab_size
-            self.lm_head = ParallelLMHead(
-                self.unpadded_vocab_size,
-                config.hidden_size,
-                org_num_embeddings=config.vocab_size,
-            )
-            self.lm_head_weight = self.lm_head.weight
-        self.logits_processor = LogitsProcessor(config.vocab_size)
-        self.sampler = Sampler()
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
-        hidden_states = self.model(
-            input_ids=input_ids,
-            positions=positions,
-            kv_caches=kv_caches,
-            attn_metadata=attn_metadata,
-        )
-        return hidden_states
-
-    def compute_logits(self, hidden_states: torch.Tensor,
-                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head_weight, hidden_states,
-                                       sampling_metadata)
-        return logits
-
-    def sample(
-        self,
-        logits: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
-    ) -> Optional[SamplerOutput]:
-        next_tokens = self.sampler(logits, sampling_metadata)
-        return next_tokens
-
-    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)
             ("qkv_proj", "q_proj", "q"),
@@ -327,21 +310,17 @@ class OlmoForCausalLM(nn.Module):
             ("gate_up_proj", "up_proj", 1),
         ]
         params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
         for name, loaded_weight in weights:
-            if "rotary_emb.inv_freq" 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
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+            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
+                if is_pp_missing_parameter(name, self):
+                    continue
                 param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
@@ -350,7 +329,84 @@ class OlmoForCausalLM(nn.Module):
                 # Skip loading extra bias for GPTQ models.
                 if name.endswith(".bias") and name not in params_dict:
                     continue
+                if is_pp_missing_parameter(name, self):
+                    continue
                 param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                 weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class OlmoForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
+    """
+    Extremely barebones HF model wrapper.
+    """
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.model = OlmoModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        if config.tie_word_embeddings:
+            self.lm_head = self.model.embed_tokens
+        else:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(
+                ["lm_head.weight"] if self.config.tie_word_embeddings else None
+            ),
+        )
+        return loader.load_weights(weights)