Add support for Cohere's Command-R model

vllm/model_executor/models/__init__.py

@@ -17,6 +17,7 @@
     "BloomForCausalLM": ("bloom", "BloomForCausalLM"),
     "ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"),
     "ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"),
+    "CohereForCausalLM": ("commandr", "CohereForCausalLM"),
     "DbrxForCausalLM": ("dbrx", "DbrxForCausalLM"),
     "DeciLMForCausalLM": ("decilm", "DeciLMForCausalLM"),
     "DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"),

vllm/model_executor/models/commandr.py

@@ -0,0 +1,337 @@
+# coding=utf-8
+# 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
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+
+# This file is based on the LLama model definition file in transformers
+"""PyTorch Cohere model."""
+from typing import List, Optional, Tuple
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from transformers import CohereConfig
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+
+from vllm.attention import Attention, AttentionMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class LayerNorm(nn.Module):
+
+    def __init__(self, hidden_size, eps=1e-5, bias=False):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.bias = nn.Parameter(torch.zeros(hidden_size)) if bias else None
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states, residuals=None):
+        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 + self.variance_epsilon)
+        hidden_states = self.weight.to(torch.float32) * hidden_states
+        if self.bias is not None:
+            hidden_states = hidden_states + self.bias.to(torch.float32)
+        return hidden_states.to(input_dtype), residuals
+
+
+ALL_LAYERNORM_LAYERS.append(LayerNorm)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaMLP Llama->Cohere
+class CohereMLP(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_up_proj = MergedColumnParallelLinear(
+            self.hidden_size,
+            [self.intermediate_size] * 2,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.down_proj = RowParallelLinear(
+            self.intermediate_size,
+            self.hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+        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 CohereAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: CohereConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        tp_size = get_tensor_model_parallel_world_size()
+        self.config = config
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.total_num_heads = config.num_attention_heads
+        self.num_heads = self.total_num_heads // tp_size
+        self.head_dim = self.hidden_size // self.total_num_heads
+        self.total_num_kv_heads = config.num_key_value_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)
+        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.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.rope_scaling = getattr(config, "rope_scaling", None)
+        self.is_causal = True
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+        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,
+            is_neox_style=False,
+        )
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        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)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class CohereDecoderLayer(nn.Module):
+
+    def __init__(self,
+                 config: CohereConfig,
+                 linear_method: Optional[LinearMethodBase] = None):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = CohereAttention(config, linear_method=linear_method)
+
+        self.mlp = CohereMLP(config, linear_method=linear_method)
+        self.input_layernorm = LayerNorm(config.hidden_size,
+                                         eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        attn_metadata: AttentionMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> 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
+        hidden_states = residual + hidden_states_attention + hidden_states_mlp
+
+        return hidden_states, residual
+
+
+class CohereModel(nn.Module):
+
+    def __init__(
+        self,
+        config: CohereConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ):
+        super().__init__()
+        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, linear_method=linear_method)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        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]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                attn_metadata,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class CohereForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: CohereConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.unpadded_vocab_size = config.vocab_size
+        self.linear_method = linear_method
+        self.logits_processor = LogitsProcessor(config.vocab_size,
+                                                scale=config.logit_scale)
+        self.model = CohereModel(config, linear_method)
+        self.sampler = Sampler()
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        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,
+        model_name_or_path: str,
+        cache_dir: Optional[str] = None,
+        load_format: str = "auto",
+        revision: Optional[str] = None,
+    ):
+        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())
+        loaded_params = set()
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            for param_name, shard_name, shard_id in stacked_params_mapping:
+                if shard_name not in name:
+                    continue
+                name = name.replace(shard_name, param_name)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)