Sync from v0.13

vllm/model_executor/models/commandr.py

@@ -1,4 +1,6 @@
-# coding=utf-8
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
 # Copyright 2024 Cohere and the HuggingFace Inc. team. All rights reserved.
 #
 # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
@@ -20,79 +22,80 @@
 
 # This file is based on the LLama model definition file in transformers
 """PyTorch Cohere model."""
-from typing import Iterable, List, Optional, Tuple
+
+from collections.abc import Iterable
+from itertools import islice
 
 import torch
-import torch.utils.checkpoint
 from torch import nn
-from torch.nn.parameter import Parameter
-from transformers import CohereConfig
+from transformers import Cohere2Config, CohereConfig
 
-from vllm.attention import Attention, AttentionMetadata
-from vllm.distributed import (get_tensor_model_parallel_rank,
-                              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 (
-    VocabParallelEmbedding)
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+    row_parallel_weight_loader,
+)
 from vllm.model_executor.utils import set_weight_attrs
-from vllm.sequence import SamplerOutput
+from vllm.platforms import current_platform
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
+from .utils import (
+    AutoWeightsLoader,
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
 
 
-@torch.compile
+@torch.compile(backend=current_platform.simple_compile_backend)
 def layer_norm_func(hidden_states, weight, variance_epsilon):
     input_dtype = hidden_states.dtype
     hidden_states = hidden_states.to(torch.float32)
     mean = hidden_states.mean(-1, keepdim=True)
     variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
-    hidden_states = (hidden_states - mean) * torch.rsqrt(variance +
-                                                         variance_epsilon)
+    hidden_states = (hidden_states - mean) * torch.rsqrt(variance + variance_epsilon)
     hidden_states = weight.to(torch.float32) * hidden_states
     return hidden_states.to(input_dtype)
 
 
 class LayerNorm(nn.Module):
-
     def __init__(self, param_shape=None, eps=1e-5):
         super().__init__()
         self.weight = nn.Parameter(torch.ones(param_shape))
         self.variance_epsilon = eps
-        set_weight_attrs(self.weight, {"weight_loader": self.weight_loader})
+        set_weight_attrs(self.weight, {"weight_loader": row_parallel_weight_loader})
 
     def forward(self, hidden_states, residuals=None):
-        hidden_states = layer_norm_func(hidden_states, self.weight,
-                                        self.variance_epsilon)
+        hidden_states = layer_norm_func(
+            hidden_states, self.weight, self.variance_epsilon
+        )
         return hidden_states, residuals
 
-    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
-        tp_rank = get_tensor_model_parallel_rank()
-        shard_dim = 0 if param.dim() != 1 else None
-        param_data = param.data
-        if shard_dim is not None:
-            shard_size = param_data.shape[shard_dim]
-            start_idx = tp_rank * shard_size
-            loaded_weight = loaded_weight.narrow(shard_dim, start_idx,
-                                                 shard_size)
-        assert param_data.shape == loaded_weight.shape
-        param_data.copy_(loaded_weight)
-
 
 # Copied from transformers.models.llama.modeling_llama.LlamaMLP Llama->Cohere
 class CohereMLP(nn.Module):
-
     def __init__(
         self,
-        config,
-        quant_config: Optional[QuantizationConfig] = None,
+        config: CohereConfig | Cohere2Config,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         self.config = config
@@ -103,12 +106,14 @@ class CohereMLP(nn.Module):
             [self.intermediate_size] * 2,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
         )
         self.down_proj = RowParallelLinear(
             self.intermediate_size,
             self.hidden_size,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
         )
         self.act_fn = SiluAndMul()
 
@@ -120,11 +125,12 @@ class CohereMLP(nn.Module):
 
 
 class CohereAttention(nn.Module):
-
     def __init__(
         self,
-        config: CohereConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        config: CohereConfig | Cohere2Config,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         tp_size = get_tensor_model_parallel_world_size()
@@ -148,10 +154,8 @@ class CohereAttention(nn.Module):
         self.kv_size = self.num_kv_heads * self.head_dim
         self.scaling = self.head_dim**-0.5
         self.max_position_embeddings = getattr(
-            config, "model_max_length", None) or getattr(
-                config, "max_position_embeddings", 8192)
-        self.rope_theta = config.rope_theta
-        self.rope_scaling = getattr(config, "rope_scaling", None)
+            config, "model_max_length", None
+        ) or getattr(config, "max_position_embeddings", 8192)
         self.use_qk_norm = getattr(config, "use_qk_norm", False)
         self.qkv_proj = QKVParallelLinear(
             self.hidden_size,
@@ -160,34 +164,49 @@ class CohereAttention(nn.Module):
             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,
             self.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=self.max_position_embeddings,
-            base=self.rope_theta,
-            rope_scaling=self.rope_scaling,
+            rope_parameters=config.rope_parameters,
             is_neox_style=False,
         )
+
+        # Model v2 has interleaved sliding windows, v1 does not
+        self.v1 = isinstance(config, CohereConfig)
+
+        self.sliding_window = None
+        if not self.v1:
+            layer_idx = extract_layer_index(prefix)
+            if config.layer_types[layer_idx] == "sliding_attention":
+                self.sliding_window = config.sliding_window
+
         self.attn = Attention(
             self.num_heads,
             self.head_dim,
             self.scaling,
             num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            per_layer_sliding_window=self.sliding_window,
+            prefix=f"{prefix}.attn",
         )
         if self.use_qk_norm:
-            self.q_norm = LayerNorm(param_shape=(self.num_heads,
-                                                 self.head_dim),
-                                    eps=config.layer_norm_eps)
-            self.k_norm = LayerNorm(param_shape=(self.num_kv_heads,
-                                                 self.head_dim),
-                                    eps=config.layer_norm_eps)
+            self.q_norm = LayerNorm(
+                param_shape=(self.num_heads, self.head_dim), eps=config.layer_norm_eps
+            )
+            self.k_norm = LayerNorm(
+                param_shape=(self.num_kv_heads, self.head_dim),
+                eps=config.layer_norm_eps,
+            )
 
     def _apply_qk_norm(self, q, k):
         q = q.view(*q.shape[:-1], -1, self.head_dim)
@@ -202,49 +221,53 @@ class CohereAttention(nn.Module):
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
         if self.use_qk_norm:
             q, k = self._apply_qk_norm(q, k)
-        q, k = self.rotary_emb(positions, q, k)
-        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        if self.v1 or self.sliding_window:
+            q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
         output, _ = self.o_proj(attn_output)
         return output
 
 
 class CohereDecoderLayer(nn.Module):
-
-    def __init__(self,
-                 config: CohereConfig,
-                 quant_config: Optional[QuantizationConfig] = None):
+    def __init__(
+        self,
+        config: CohereConfig | Cohere2Config,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
         super().__init__()
         self.hidden_size = config.hidden_size
 
-        self.self_attn = CohereAttention(config, quant_config=quant_config)
+        self.self_attn = CohereAttention(
+            config,
+            cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
 
-        self.mlp = CohereMLP(config, quant_config=quant_config)
-        self.input_layernorm = LayerNorm(param_shape=(config.hidden_size),
-                                         eps=config.layer_norm_eps)
+        self.mlp = CohereMLP(config, quant_config=quant_config, prefix=f"{prefix}.mlp")
+        self.input_layernorm = LayerNorm(
+            param_shape=(config.hidden_size), eps=config.layer_norm_eps
+        )
 
     def forward(
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
-        residual: Optional[torch.Tensor],
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        residual: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         # Self Attention
         residual = hidden_states
         hidden_states, residual = self.input_layernorm(hidden_states, residual)
         hidden_states_attention = self.self_attn(
             positions=positions,
             hidden_states=hidden_states,
-            kv_cache=kv_cache,
-            attn_metadata=attn_metadata,
         )
         hidden_states_mlp = self.mlp(hidden_states)
         # Add everything together
@@ -253,89 +276,71 @@ class CohereDecoderLayer(nn.Module):
         return hidden_states, residual
 
 
+@support_torch_compile
 class CohereModel(nn.Module):
-
-    def __init__(
-        self,
-        config: CohereConfig,
-        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.quant_config = quant_config
+
         self.config = config
+
         self.vocab_size = config.vocab_size
-        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
-                                                   config.hidden_size)
-        self.layers = nn.ModuleList([
-            CohereDecoderLayer(config, quant_config=quant_config)
-            for _ in range(config.num_hidden_layers)
-        ])
-        self.norm = LayerNorm(param_shape=(config.hidden_size),
-                              eps=config.layer_norm_eps)
+
+        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: CohereDecoderLayer(
+                config, cache_config, quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers",
+        )
+        self.norm = LayerNorm(
+            param_shape=(config.hidden_size), eps=config.layer_norm_eps
+        )
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], 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:
-        hidden_states = self.embed_tokens(input_ids)
-        residual = None
-        for i in range(len(self.layers)):
-            layer = self.layers[i]
+        intermediate_tensors: IntermediateTensors | None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        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)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
             hidden_states, residual = layer(
                 positions,
                 hidden_states,
-                kv_caches[i],
-                attn_metadata,
                 residual,
             )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
-
-class CohereForCausalLM(nn.Module):
-
-    def __init__(
-        self,
-        config: CohereConfig,
-        quant_config: Optional[QuantizationConfig] = None,
-    ) -> None:
-        super().__init__()
-        self.config = config
-        self.quant_config = quant_config
-        self.logits_processor = LogitsProcessor(config.vocab_size,
-                                                scale=config.logit_scale)
-        self.model = CohereModel(config, quant_config)
-        self.sampler = Sampler()
-
-    @torch.no_grad()
-    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, positions, kv_caches,
-                                   attn_metadata)
-        return hidden_states
-
-    def compute_logits(self, hidden_states: torch.Tensor,
-                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.model.embed_tokens.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"),
@@ -345,8 +350,21 @@ class CohereForCausalLM(nn.Module):
             ("gate_up_proj", "up_proj", 1),
         ]
         params_dict = dict(self.named_parameters())
-        loaded_params = set()
+        loaded_params: set[str] = set()
         for name, loaded_weight in weights:
+            if self.quant_config is not None and (
+                scale_name := self.quant_config.get_cache_scale(name)
+            ):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                loaded_weight = (
+                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
+                )
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
             for param_name, shard_name, shard_id in stacked_params_mapping:
                 if shard_name not in name:
                     continue
@@ -354,20 +372,98 @@ class CohereForCausalLM(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 = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
-                # lm_head is not used in vllm as it is tied with embed_token.
-                # To prevent errors, skip loading lm_head.weight.
-                if "lm_head.weight" in name:
-                    continue
                 # Skip loading extra bias for GPTQ models.
                 if name.endswith(".bias") and name not in params_dict:
                     continue
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    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 CohereForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsQuant):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+    # LoRA specific attributes
+    embedding_modules = {"embed_tokens": "input_embeddings"}
+
+    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
+        # currently all existing command R models have `tie_word_embeddings`
+        # enabled
+        assert config.tie_word_embeddings
+
+        self.quant_config = quant_config
+        self.logits_processor = LogitsProcessor(
+            config.vocab_size, scale=config.logit_scale
+        )
+        self.model = CohereModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        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)
+
+    @torch.no_grad()
+    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, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        is_not_lora = hasattr(self.model.embed_tokens, "weight")
+        if is_not_lora:
+            logits = self.logits_processor(self.model.embed_tokens, hidden_states)
+        else:
+            logits = self.logits_processor(
+                self.model.embed_tokens.base_layer, hidden_states
+            )
+
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self, skip_prefixes=["lm_head", "rotary_emb.inv_freq"]
+        )
+        return loader.load_weights(weights)