[Quality] Add code formatter and linter (#326)

vllm/model_executor/__init__.py

@@ -2,7 +2,6 @@ from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.model_loader import get_model
 from vllm.model_executor.utils import set_random_seed
 
-
 __all__ = [
     "InputMetadata",
     "get_model",

vllm/model_executor/input_metadata.py

@@ -8,11 +8,22 @@ from vllm.sequence import SequenceData
 
 
 class InputMetadata:
+    """Metadata for input sequences. Used for PagedAttention.
+
+    Args:
+        seq_groups: List of (seq_ids, sampling_params).
+        seq_data: Seq_id -> SequenceData.
+        prompt_lens: Lengths of prompts.
+        slot_mapping: The address to write the new KV to of each token.
+        context_lens: the length of attention context for each generation token.
+        max_context_len: The maximum context length.
+        block_tables: The block tables. (Seq id -> list of physical block)
+    """
 
     def __init__(
         self,
-        seq_groups: List[Tuple[List[int], SamplingParams]],     # List of (seq_ids, sampling_params).
-        seq_data: Dict[int, SequenceData],                      # Seq_id -> SequenceData.
+        seq_groups: List[Tuple[List[int], SamplingParams]],
+        seq_data: Dict[int, SequenceData],
         prompt_lens: List[int],
         slot_mapping: torch.Tensor,
         context_lens: torch.Tensor,

vllm/model_executor/layers/activation.py

@@ -6,9 +6,10 @@ from vllm import activation_ops
 
 _ACTIVATION_REGISTRY = {
     "gelu": nn.GELU(),
-    "gelu_new": nn.GELU(approximate="tanh"),   # NOTE: This may introduce small rounding errors.
-    "gelu_fast": nn.GELU(approximate="tanh"),  # NOTE: This may introduce small rounding errors.
-    "gelu_pytorch_tanh": nn.GELU(approximate="tanh"),  # NOTE: This may introduce small rounding errors.
+    # NOTE: The following GELU functions may introduce small rounding errors.
+    "gelu_new": nn.GELU(approximate="tanh"),
+    "gelu_fast": nn.GELU(approximate="tanh"),
+    "gelu_pytorch_tanh": nn.GELU(approximate="tanh"),
     "relu": nn.ReLU(),
 }
 
@@ -25,15 +26,13 @@ class SiluAndMul(nn.Module):
     """An activation function for SwiGLU.
 
     The function computes x -> silu(x[:d]) * x[d:] where d = x.shape[1] // 2.
+
+    Shapes:
+        x: (num_tokens, 2 * d)
+        return: (num_tokens, d)
     """
 
-    def __init__(self):
-        super().__init__()
-
-    def forward(
-        self,
-        x: torch.Tensor,        # (num_tokens, 2 * d)
-    ) -> torch.Tensor:          # (num_tokens, d)
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         num_tokens = x.shape[0]
         d = x.shape[1] // 2
         out = torch.empty(num_tokens, d, dtype=x.dtype, device=x.device)

vllm/model_executor/layers/attention.py

@@ -14,6 +14,7 @@ _SUPPORTED_HEAD_SIZES = [64, 80, 96, 128]
 
 
 class PagedAttention(nn.Module):
+    # pylint: disable=line-too-long
     """GPT-style multi-head PagedAttention.
 
     This class takes flattened 1D query, key, and value tensors as input. The
@@ -54,12 +55,20 @@ class PagedAttention(nn.Module):
 
     def multi_query_kv_attention(
         self,
-        output: torch.Tensor,                   # [num_prompt_tokens, num_heads, head_size]
-        query: torch.Tensor,                    # [num_prompt_tokens, num_heads, head_size]
-        key: torch.Tensor,                      # [num_prompt_tokens, num_heads, head_size]
-        value: torch.Tensor,                    # [num_prompt_tokens, num_heads, head_size]
+        output: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
         attn_bias: xops.AttentionBias,
     ) -> torch.Tensor:
+        """Normal attention for the prompt tokens.
+
+        Args:
+            output: shape = [num_prompt_tokens, num_heads, head_size]
+            query: shape = [num_prompt_tokens, num_heads, head_size]
+            key: shape = [num_prompt_tokens, num_heads, head_size]
+            value: shape = [num_prompt_tokens, num_heads, head_size]
+        """
         # TODO(woosuk): The unsqueeze op may incur some CPU overhead. Optimize.
         out = xops.memory_efficient_attention_forward(
             query.unsqueeze(0),
@@ -76,12 +85,22 @@ class PagedAttention(nn.Module):
 
     def single_query_cached_kv_attention(
         self,
-        output: torch.Tensor,           # [num_generation_tokens, num_heads, head_size]
-        query: torch.Tensor,            # [num_generation_tokens, num_heads, head_size]
-        key_cache: torch.Tensor,        # [num_blocks, num_heads, head_size/x, block_size, x]
-        value_cache: torch.Tensor,      # [num_blocks, num_heads, head_size, block_size]
+        output: torch.Tensor,
+        query: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
         input_metadata: InputMetadata,
     ) -> None:
+        """PagedAttention for the generation tokens.
+
+        Args:
+            output: shape = [num_generation_tokens, num_heads, head_size]
+            query: shape = [num_generation_tokens, num_heads, head_size]
+            key_cache: shape = [num_blocks, num_heads, head_size/x,
+                block_size, x]
+            value_cache: shape = [num_blocks, num_heads, head_size, block_size]
+            input_metadata: metadata for paged attention.
+        """
         block_size = value_cache.shape[3]
         attention_ops.single_query_cached_kv_attention(
             output,
@@ -97,16 +116,32 @@ class PagedAttention(nn.Module):
 
     def forward(
         self,
-        query: torch.Tensor,                    # [num_tokens, num_heads * head_size]
-        key: torch.Tensor,                      # [num_tokens, num_heads * head_size]
-        value: torch.Tensor,                    # [num_tokens, num_heads * head_size]
-        key_cache: Optional[torch.Tensor],      # [num_blocks, num_heads, head_size/x, block_size, x]
-        value_cache: Optional[torch.Tensor],    # [num_blocks, num_heads, head_size, block_size]
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        key_cache: Optional[torch.Tensor],
+        value_cache: Optional[torch.Tensor],
         input_metadata: InputMetadata,
         cache_event: Optional[torch.cuda.Event],
-    ) -> torch.Tensor:                          # [num_tokens, num_heads * head_size]
-        # NOTE: The query, key, and value tensors must be sliced from a qkv
-        # tensor of shape [num_tokens, 3 * num_heads * head_size].
+    ) -> torch.Tensor:
+        """PagedAttention forward pass.
+
+        NOTE: The query, key, and value tensors must be sliced from a qkv
+        tensor of shape [num_tokens, 3 * num_heads * head_size].
+
+        Args:
+            query: shape = [num_tokens, num_heads * head_size]
+            key: shape = [num_tokens, num_heads * head_size]
+            value: shape = [num_tokens, num_heads * head_size]
+            key_cache: shape = [num_blocks, num_heads, head_size/x,
+                block_size, x]
+            value_cache: shape = [num_blocks, num_heads, head_size, block_size]
+            input_metadata: metadata for paged attention.
+            cache_event: event to wait for the cache operations to finish.
+
+        Returns:
+            shape = [num_tokens, num_heads * head_size]
+        """
 
         # Reshape the query, key, and value tensors.
         query = query.view(-1, self.num_heads, self.head_size)
@@ -136,7 +171,7 @@ class PagedAttention(nn.Module):
         # and value vectors will not be cached.
         num_valid_tokens = input_metadata.num_valid_tokens
         if (num_valid_tokens > 0 and key_cache is not None
-            and value_cache is not None):
+                and value_cache is not None):
             # The stride is 3 because the key and value are sliced from qkv.
             cache_ops.reshape_and_cache(
                 key[:num_valid_tokens],
@@ -149,15 +184,12 @@ class PagedAttention(nn.Module):
         if input_metadata.num_generation_tokens > 0:
             assert key_cache is not None and value_cache is not None, (
                 "key_cache and value_cache must be provided when "
-                "generating tokens."
-            )
+                "generating tokens.")
             # Compute the attention op for generation tokens.
             self.single_query_cached_kv_attention(
                 output[num_prompt_tokens:num_valid_tokens],
-                query[num_prompt_tokens:num_valid_tokens],
-                key_cache,
-                value_cache,
-                input_metadata)
+                query[num_prompt_tokens:num_valid_tokens], key_cache,
+                value_cache, input_metadata)
 
         # Reshape the output tensor.
         # NOTE(woosuk): The output tensor may include paddings.
@@ -179,9 +211,9 @@ class PagedAttentionWithRoPE(PagedAttention):
         super().__init__(num_heads, head_size, scale)
 
         # Create the cos and sin cache.
-        inv_freq = 1.0 / (base ** (torch.arange(0, rotary_dim, 2) / rotary_dim))
+        inv_freq = 1.0 / (base**(torch.arange(0, rotary_dim, 2) / rotary_dim))
         t = torch.arange(max_position).float()
-        freqs = torch.einsum('i,j -> ij', t, inv_freq.float())
+        freqs = torch.einsum("i,j -> ij", t, inv_freq.float())
         cos = freqs.cos()
         sin = freqs.sin()
         cache = torch.cat((cos, sin), dim=-1)
@@ -195,15 +227,32 @@ class PagedAttentionWithRoPE(PagedAttention):
 
     def forward(
         self,
-        positions: torch.Tensor,                # [num_tokens]
-        query: torch.Tensor,                    # [num_tokens, num_heads * head_size]
-        key: torch.Tensor,                      # [num_tokens, num_heads * head_size]
-        value: torch.Tensor,                    # [num_tokens, num_heads * head_size]
-        key_cache: torch.Tensor,                # [num_blocks, num_heads, head_size/x, block_size, x]
-        value_cache: torch.Tensor,              # [num_blocks, num_heads, head_size, block_size]
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
         input_metadata: InputMetadata,
         cache_event: Optional[torch.cuda.Event],
-    ) -> torch.Tensor:                          # [num_tokens, num_heads * head_size]
+    ) -> torch.Tensor:
+        """ PagedAttention forward pass with rotary embedding.
+
+        Args:
+            positions: shape = [num_tokens]
+                        query: shape = [num_tokens, num_heads * head_size]
+            key: shape = [num_tokens, num_heads * head_size]
+            value: shape = [num_tokens, num_heads * head_size]
+            key_cache: shape = [num_blocks, num_heads, head_size/x,
+                block_size, x]
+            value_cache: shape = [num_blocks, num_heads, head_size, block_size]
+            input_metadata: metadata for paged attention.
+            cache_event: event to wait for the cache operations to finish.
+
+        Returns:
+            shape = [num_tokens, num_heads * head_size]
+        """
+
         # Apply rotary embedding to the query and key before passing them
         # to the attention op.
         pos_encoding_ops.rotary_embedding_neox(

vllm/model_executor/layers/sampler.py

@@ -13,6 +13,7 @@ from vllm.sequence import SequenceOutputs
 
 _SAMPLING_EPS = 1e-5
 
+
 class Sampler(nn.Module):
     """Samples the next tokens from the model's outputs.
 
@@ -50,19 +51,20 @@ class Sampler(nn.Module):
         # Apply presence and frequency penalties.
         output_tokens = _get_output_tokens(input_metadata)
         assert len(output_tokens) == logits.shape[0]
-        presence_penalties, frequency_penalties = _get_penalties(input_metadata)
+        presence_penalties, frequency_penalties = _get_penalties(
+            input_metadata)
         assert len(presence_penalties) == logits.shape[0]
         assert len(frequency_penalties) == logits.shape[0]
-        logits = _apply_penalties(
-            logits, output_tokens, presence_penalties, frequency_penalties,
-            self.vocab_size)
+        logits = _apply_penalties(logits, output_tokens, presence_penalties,
+                                  frequency_penalties, self.vocab_size)
 
         # Apply temperature scaling.
         temperatures = _get_temperatures(input_metadata)
         assert len(temperatures) == logits.shape[0]
         if any(t != 1.0 for t in temperatures):
-            t = torch.tensor(
-                temperatures, dtype=logits.dtype, device=logits.device)
+            t = torch.tensor(temperatures,
+                             dtype=logits.dtype,
+                             device=logits.device)
             # Use in-place division to avoid creating a new tensor.
             logits.div_(t.unsqueeze(dim=1))
 
@@ -75,7 +77,9 @@ class Sampler(nn.Module):
         # Apply top-p and top-k truncation.
         top_ps, top_ks = _get_top_p_top_k(input_metadata, self.vocab_size)
         assert len(top_ps) == len(top_ks) == probs.shape[0]
-        if any(p < 1.0 - _SAMPLING_EPS for p in top_ps) or any(k != self.vocab_size for k in top_ks):
+        do_top_p = any(p < 1.0 - _SAMPLING_EPS for p in top_ps)
+        do_top_k = any(k != self.vocab_size for k in top_ks)
+        if do_top_p or do_top_k:
             probs = _apply_top_p_top_k(probs, top_ps, top_ks)
 
         # Sample the next tokens.
@@ -97,8 +101,7 @@ def _prune_hidden_states(
 
 
 def _get_penalties(
-    input_metadata: InputMetadata,
-) -> Tuple[List[float], List[float]]:
+        input_metadata: InputMetadata) -> Tuple[List[float], List[float]]:
     # Collect the presence and frequency penalties.
     presence_penalties: List[float] = []
     frequency_penalties: List[float] = []
@@ -117,9 +120,7 @@ def _get_penalties(
     return presence_penalties, frequency_penalties
 
 
-def _get_output_tokens(
-    input_metadata: InputMetadata,
-) -> List[List[int]]:
+def _get_output_tokens(input_metadata: InputMetadata) -> List[List[int]]:
     output_tokens: List[List[int]] = []
     for i, seq_group in enumerate(input_metadata.seq_groups):
         seq_ids, _ = seq_group
@@ -169,11 +170,13 @@ def _apply_penalties(
                                                  device=logits.device)
 
     frequency_penalties = [frequency_penalties[i] for i in indices]
-    frequency_penalties = torch.tensor(
-        frequency_penalties, dtype=logits.dtype, device=logits.device)
+    frequency_penalties = torch.tensor(frequency_penalties,
+                                       dtype=logits.dtype,
+                                       device=logits.device)
     presence_penalties = [presence_penalties[i] for i in indices]
-    presence_penalties = torch.tensor(
-        presence_penalties, dtype=logits.dtype, device=logits.device)
+    presence_penalties = torch.tensor(presence_penalties,
+                                      dtype=logits.dtype,
+                                      device=logits.device)
 
     # We follow the definition in OpenAI API.
     # Refer to https://platform.openai.com/docs/api-reference/parameter-details
@@ -183,9 +186,7 @@ def _apply_penalties(
     return logits
 
 
-def _get_temperatures(
-    input_metadata: InputMetadata,
-) -> List[float]:
+def _get_temperatures(input_metadata: InputMetadata) -> List[float]:
     # Collect the temperatures for the logits.
     temperatures: List[float] = []
     for i, seq_group in enumerate(input_metadata.seq_groups):
@@ -252,8 +253,9 @@ def _apply_top_p_top_k(
     probs_sort[top_k_mask] = 0.0
 
     # Re-sort the probabilities.
-    probs = torch.gather(
-        probs_sort, dim=-1, index=torch.argsort(probs_idx, dim=-1))
+    probs = torch.gather(probs_sort,
+                         dim=-1,
+                         index=torch.argsort(probs_idx, dim=-1))
     return probs
 
 
@@ -296,8 +298,9 @@ def _sample_from_prompt(
         # Random sampling.
         # Sample `best_of` tokens for the prompt.
         num_seqs = sampling_params.best_of
-        next_token_ids = torch.multinomial(
-            prob, num_samples=num_seqs, replacement=True)
+        next_token_ids = torch.multinomial(prob,
+                                           num_samples=num_seqs,
+                                           replacement=True)
         next_token_ids = next_token_ids.tolist()
     return next_token_ids
 
@@ -315,8 +318,9 @@ def _sample_from_generation_tokens(
     if sampling_params.use_beam_search:
         # Beam search.
         # Add cumulative logprobs for the sequences in the group.
-        seq_logprobs = torch.tensor(
-            seq_logprobs, dtype=torch.float, device=logprobs.device)
+        seq_logprobs = torch.tensor(seq_logprobs,
+                                    dtype=torch.float,
+                                    device=logprobs.device)
         logprobs = logprobs + seq_logprobs.unsqueeze(dim=1)
 
         vocab_size = logprobs.size(-1)
@@ -353,8 +357,9 @@ def _sample_from_generation_tokens(
     else:
         # Random sampling.
         # Sample 1 token for each sequence in the group.
-        next_token_ids = torch.multinomial(
-            probs, num_samples=1, replacement=True)
+        next_token_ids = torch.multinomial(probs,
+                                           num_samples=1,
+                                           replacement=True)
         next_token_ids = next_token_ids.squeeze(dim=-1).tolist()
         parent_seq_ids = seq_ids
     return parent_seq_ids, next_token_ids
@@ -381,15 +386,16 @@ def _sample(
             # Sample the next tokens.
             next_token_ids = _sample_from_prompt(prob, sampling_params)
             # Get top-k log probabilities for the next tokens.
-            next_logprobs = _get_topk_logprobs(
-                logprob, sampling_params.logprobs)
+            next_logprobs = _get_topk_logprobs(logprob,
+                                               sampling_params.logprobs)
 
             # Build the output.
             for seq_id, next_token_id in zip(seq_ids, next_token_ids):
                 output_logprobs = next_logprobs.copy()
                 output_logprobs[next_token_id] = logprob[next_token_id].item()
-                seq_outputs[seq_id] = SequenceOutputs(
-                    seq_id, seq_id, next_token_id, output_logprobs)
+                seq_outputs[seq_id] = SequenceOutputs(seq_id, seq_id,
+                                                      next_token_id,
+                                                      output_logprobs)
         else:
             # Generate the next tokens for generation tokens.
             prob = probs[idx:idx + len(seq_ids)]
@@ -399,22 +405,24 @@ def _sample(
             # Sample the next tokens.
             seq_logprobs = [
                 input_metadata.seq_data[seq_id].cumulative_logprob
-                for seq_id in seq_ids]
+                for seq_id in seq_ids
+            ]
             parent_seq_ids, next_token_ids = _sample_from_generation_tokens(
                 seq_ids, prob, logprob, seq_logprobs, sampling_params)
 
             # Get top-k log probabilities for the next tokens.
             next_logprobs: Dict[int, Dict[int, float]] = {}
-            for i, seq_id in enumerate(seq_ids):
+            for j, seq_id in enumerate(seq_ids):
                 next_logprobs[seq_id] = _get_topk_logprobs(
-                    logprob[i], sampling_params.logprobs)
+                    logprob[j], sampling_params.logprobs)
 
             # Build the output.
             for seq_id, parent_seq_id, next_token_id in zip(
-                seq_ids, parent_seq_ids, next_token_ids):
-                i = seq_ids.index(parent_seq_id)
+                    seq_ids, parent_seq_ids, next_token_ids):
+                j = seq_ids.index(parent_seq_id)
                 output_logprobs = next_logprobs[parent_seq_id].copy()
-                output_logprobs[next_token_id] = logprob[i, next_token_id].item()
+                output_logprobs[next_token_id] = logprob[j,
+                                                         next_token_id].item()
                 seq_outputs[seq_id] = SequenceOutputs(
                     seq_id,
                     parent_seq_id,

vllm/model_executor/model_loader.py

@@ -6,8 +6,9 @@ import torch.nn as nn
 from transformers import PretrainedConfig
 
 from vllm.config import ModelConfig
-from vllm.model_executor.models import (GPT2LMHeadModel, GPTBigCodeForCausalLM, GPTNeoXForCausalLM,
-                                        LlamaForCausalLM, OPTForCausalLM)
+from vllm.model_executor.models import (GPT2LMHeadModel, GPTBigCodeForCausalLM,
+                                        GPTNeoXForCausalLM, LlamaForCausalLM,
+                                        OPTForCausalLM)
 from vllm.model_executor.weight_utils import initialize_dummy_weights
 
 # TODO(woosuk): Lazy-load the model classes.
@@ -28,8 +29,7 @@ def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
             return _MODEL_REGISTRY[arch]
     raise ValueError(
         f"Model architectures {architectures} are not supported for now. "
-        f"Supported architectures: {list(_MODEL_REGISTRY.keys())}"
-    )
+        f"Supported architectures: {list(_MODEL_REGISTRY.keys())}")
 
 
 def get_model(model_config: ModelConfig) -> nn.Module:
@@ -46,8 +46,7 @@ def get_model(model_config: ModelConfig) -> nn.Module:
         initialize_dummy_weights(model)
     else:
         # Load the weights from the cached or downloaded files.
-        model.load_weights(
-            model_config.model, model_config.download_dir,
-            model_config.use_np_weights)
+        model.load_weights(model_config.model, model_config.download_dir,
+                           model_config.use_np_weights)
         model = model.cuda()
     return model.eval()

vllm/model_executor/models/__init__.py

@@ -4,8 +4,6 @@ from vllm.model_executor.models.gpt_neox import GPTNeoXForCausalLM
 from vllm.model_executor.models.llama import LlamaForCausalLM
 from vllm.model_executor.models.opt import OPTForCausalLM
 
-
-
 __all__ = [
     "GPT2LMHeadModel",
     "GPTBigCodeForCausalLM",

vllm/model_executor/models/gpt2.py

@@ -1,5 +1,6 @@
 # coding=utf-8
-# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
 # Copyright 2023 The vLLM team.
 # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
@@ -47,19 +48,25 @@ class GPT2Attention(nn.Module):
         super().__init__()
         self.hidden_size = config.hidden_size
         total_num_heads = config.num_attention_heads
-        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
         assert total_num_heads % tensor_model_parallel_world_size == 0
         self.num_heads = total_num_heads // tensor_model_parallel_world_size
         self.head_dim = self.hidden_size // total_num_heads
-        self.scale = self.head_dim ** -0.5
+        self.scale = self.head_dim**-0.5
 
-        self.c_attn = ColumnParallelLinear(self.hidden_size, 3 * self.hidden_size,
-                                           bias=True, gather_output=False,
+        self.c_attn = ColumnParallelLinear(self.hidden_size,
+                                           3 * self.hidden_size,
+                                           bias=True,
+                                           gather_output=False,
                                            perform_initialization=False)
-        self.c_proj = RowParallelLinear(self.hidden_size, self.hidden_size,
-                                        bias=True, input_is_parallel=True,
+        self.c_proj = RowParallelLinear(self.hidden_size,
+                                        self.hidden_size,
+                                        bias=True,
+                                        input_is_parallel=True,
                                         perform_initialization=False)
-        self.attn = PagedAttention(self.num_heads, self.head_dim,
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
                                    scale=self.scale)
 
     def forward(
@@ -72,8 +79,8 @@ class GPT2Attention(nn.Module):
         qkv, _ = self.c_attn(hidden_states)
         q, k, v = qkv.chunk(chunks=3, dim=-1)
         key_cache, value_cache = kv_cache
-        attn_output = self.attn(
-            q, k, v, key_cache, value_cache, input_metadata, cache_event)
+        attn_output = self.attn(q, k, v, key_cache, value_cache,
+                                input_metadata, cache_event)
         attn_output, _ = self.c_proj(attn_output)
         return attn_output
 
@@ -87,11 +94,15 @@ class GPT2MLP(nn.Module):
     ):
         super().__init__()
         hidden_size = config.hidden_size
-        self.c_fc = ColumnParallelLinear(hidden_size, intermediate_size,
-                                         bias=True, gather_output=False,
+        self.c_fc = ColumnParallelLinear(hidden_size,
+                                         intermediate_size,
+                                         bias=True,
+                                         gather_output=False,
                                          perform_initialization=False)
-        self.c_proj = RowParallelLinear(intermediate_size, hidden_size,
-                                        bias=True, input_is_parallel=True,
+        self.c_proj = RowParallelLinear(intermediate_size,
+                                        hidden_size,
+                                        bias=True,
+                                        input_is_parallel=True,
                                         perform_initialization=False)
         self.act = get_act_fn(config.activation_function)
 
@@ -107,7 +118,8 @@ class GPT2Block(nn.Module):
     def __init__(self, config: GPT2Config):
         super().__init__()
         hidden_size = config.hidden_size
-        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
+        inner_dim = (config.n_inner if config.n_inner is not None else 4 *
+                     hidden_size)
 
         self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
         self.attn = GPT2Attention(config)
@@ -145,9 +157,9 @@ class GPT2Model(nn.Module):
     def __init__(self, config: GPT2Config):
         super().__init__()
         self.config = config
-        assert config.add_cross_attention == False
-        assert config.scale_attn_by_inverse_layer_idx == False
-        assert config.reorder_and_upcast_attn == False
+        assert not config.add_cross_attention
+        assert not config.scale_attn_by_inverse_layer_idx
+        assert not config.reorder_and_upcast_attn
         self.embed_dim = config.hidden_size
 
         # Optimization: While the vocab size of GPT-2 is 50257, we extend it
@@ -180,8 +192,8 @@ class GPT2Model(nn.Module):
             else:
                 cache_event = cache_events[i]
             layer = self.h[i]
-            hidden_states = layer(
-                hidden_states, kv_caches[i], input_metadata, cache_event)
+            hidden_states = layer(hidden_states, kv_caches[i], input_metadata,
+                                  cache_event)
 
         hidden_states = self.ln_f(hidden_states)
         return hidden_states
@@ -206,24 +218,26 @@ class GPT2LMHeadModel(nn.Module):
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
     ) -> Dict[int, SequenceOutputs]:
-        hidden_states = self.transformer(
-            input_ids, positions, kv_caches, input_metadata, cache_events)
-        next_tokens = self.sampler(
-            self.lm_head_weight, hidden_states, input_metadata)
+        hidden_states = self.transformer(input_ids, positions, kv_caches,
+                                         input_metadata, cache_events)
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   input_metadata)
         return next_tokens
 
     _column_parallel_weights = ["wte.weight", "c_fc.weight", "c_fc.bias"]
     _row_parallel_weights = ["c_proj.weight"]
 
-    def load_weights(self, model_name_or_path: str,
+    def load_weights(self,
+                     model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      use_np_cache: bool = False):
-        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-            model_name_or_path, cache_dir, use_np_cache):
+                model_name_or_path, cache_dir, use_np_cache):
             if "lm_head.weight" in name:
                 # GPT-2 ties the weights of the embedding layer and the final
                 # linear layer.
@@ -248,16 +262,20 @@ class GPT2LMHeadModel(nn.Module):
 
             if name == "transformer.wte.weight":
                 # Consider padding in the vocab size.
-                padded_vocab_size = param.shape[0] * tensor_model_parallel_world_size
+                padded_vocab_size = (param.shape[0] *
+                                     tensor_model_parallel_world_size)
                 num_extra_rows = padded_vocab_size - self.config.vocab_size
-                extra_rows = torch.empty(num_extra_rows, loaded_weight.shape[1])
+                extra_rows = torch.empty(num_extra_rows,
+                                         loaded_weight.shape[1])
                 extra_rows = extra_rows.to(loaded_weight)
                 loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
 
             # For the fused QKV linear layer, manually shard the weights.
             if "c_attn" in name:
-                # GPT-2's fused QKV has the shape of [3 * num_heads * head_size, hidden_size].
-                # When tensor parallelism is used, we shard the weights along the head dimension.
+                # GPT-2's fused QKV has the shape of
+                # [3 * num_heads * head_size, hidden_size].
+                # When tensor parallelism is used, we shard the weights along
+                # the head dimension.
                 total_num_heads = self.config.num_attention_heads
                 hidden_size = self.config.hidden_size
                 head_size = hidden_size // total_num_heads
@@ -266,11 +284,13 @@ class GPT2LMHeadModel(nn.Module):
                 head_end = (tensor_model_parallel_rank + 1) * num_heads
 
                 if name.endswith(".weight"):
-                    loaded_weight = loaded_weight.view(3, total_num_heads, head_size, hidden_size)
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size, hidden_size)
                     loaded_weight = loaded_weight[:, head_start:head_end, :, :]
                     loaded_weight = loaded_weight.reshape(-1, hidden_size)
                 elif name.endswith(".bias"):
-                    loaded_weight = loaded_weight.view(3, total_num_heads, head_size)
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size)
                     loaded_weight = loaded_weight[:, head_start:head_end, :]
                     loaded_weight = loaded_weight.reshape(-1)
                 else:

vllm/model_executor/models/gpt_bigcode.py

@@ -1,5 +1,6 @@
 # coding=utf-8
-# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
 # Copyright 2023 The vLLM team.
 # Copyright 2023 CTranslate2, and Michael Feil
 # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
@@ -49,19 +50,25 @@ class GPTBigCodeAttention(nn.Module):
         super().__init__()
         self.hidden_size = config.hidden_size
         total_num_heads = config.num_attention_heads
-        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
         assert total_num_heads % tensor_model_parallel_world_size == 0
         self.num_heads = total_num_heads // tensor_model_parallel_world_size
         self.head_dim = self.hidden_size // total_num_heads
-        self.scale = self.head_dim ** -0.5
+        self.scale = self.head_dim**-0.5
 
-        self.c_attn = ColumnParallelLinear(self.hidden_size, 3 * self.hidden_size,
-                                           bias=True, gather_output=False,
+        self.c_attn = ColumnParallelLinear(self.hidden_size,
+                                           3 * self.hidden_size,
+                                           bias=True,
+                                           gather_output=False,
                                            perform_initialization=False)
-        self.c_proj = RowParallelLinear(self.hidden_size, self.hidden_size,
-                                        bias=True, input_is_parallel=True,
+        self.c_proj = RowParallelLinear(self.hidden_size,
+                                        self.hidden_size,
+                                        bias=True,
+                                        input_is_parallel=True,
                                         perform_initialization=False)
-        self.attn = PagedAttention(self.num_heads, self.head_dim,
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
                                    scale=self.scale)
 
     def forward(
@@ -74,8 +81,8 @@ class GPTBigCodeAttention(nn.Module):
         qkv, _ = self.c_attn(hidden_states)
         q, k, v = qkv.chunk(chunks=3, dim=-1)
         key_cache, value_cache = kv_cache
-        attn_output = self.attn(
-            q, k, v, key_cache, value_cache, input_metadata, cache_event)
+        attn_output = self.attn(q, k, v, key_cache, value_cache,
+                                input_metadata, cache_event)
         attn_output, _ = self.c_proj(attn_output)
         return attn_output
 
@@ -89,11 +96,15 @@ class GPTBigMLP(nn.Module):
     ):
         super().__init__()
         hidden_size = config.hidden_size
-        self.c_fc = ColumnParallelLinear(hidden_size, intermediate_size,
-                                         bias=True, gather_output=False,
+        self.c_fc = ColumnParallelLinear(hidden_size,
+                                         intermediate_size,
+                                         bias=True,
+                                         gather_output=False,
                                          perform_initialization=False)
-        self.c_proj = RowParallelLinear(intermediate_size, hidden_size,
-                                        bias=True, input_is_parallel=True,
+        self.c_proj = RowParallelLinear(intermediate_size,
+                                        hidden_size,
+                                        bias=True,
+                                        input_is_parallel=True,
                                         perform_initialization=False)
         self.act = get_act_fn(config.activation_function)
 
@@ -109,7 +120,8 @@ class GPTBigCodeBlock(nn.Module):
     def __init__(self, config: GPTBigCodeConfig):
         super().__init__()
         hidden_size = config.hidden_size
-        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
+        inner_dim = (config.n_inner if config.n_inner is not None else 4 *
+                     hidden_size)
 
         self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
         self.attn = GPTBigCodeAttention(config)
@@ -147,7 +159,7 @@ class GPTBigCodeModel(nn.Module):
     def __init__(self, config: GPTBigCodeConfig):
         super().__init__()
         self.config = config
-        assert config.add_cross_attention == False
+        assert not config.add_cross_attention
 
         self.embed_dim = config.hidden_size
 
@@ -181,8 +193,8 @@ class GPTBigCodeModel(nn.Module):
             else:
                 cache_event = cache_events[i]
             layer = self.h[i]
-            hidden_states = layer(
-                hidden_states, kv_caches[i], input_metadata, cache_event)
+            hidden_states = layer(hidden_states, kv_caches[i], input_metadata,
+                                  cache_event)
 
         hidden_states = self.ln_f(hidden_states)
         return hidden_states
@@ -207,24 +219,26 @@ class GPTBigCodeForCausalLM(nn.Module):
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
     ) -> Dict[int, SequenceOutputs]:
-        hidden_states = self.transformer(
-            input_ids, positions, kv_caches, input_metadata, cache_events)
-        next_tokens = self.sampler(
-            self.lm_head_weight, hidden_states, input_metadata)
+        hidden_states = self.transformer(input_ids, positions, kv_caches,
+                                         input_metadata, cache_events)
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   input_metadata)
         return next_tokens
 
     _column_parallel_weights = ["wte.weight", "c_fc.weight", "c_fc.bias"]
     _row_parallel_weights = ["c_proj.weight"]
 
-    def load_weights(self, model_name_or_path: str,
+    def load_weights(self,
+                     model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      use_np_cache: bool = False):
-        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-            model_name_or_path, cache_dir, use_np_cache):
+                model_name_or_path, cache_dir, use_np_cache):
             if "lm_head.weight" in name:
                 # GPT-2 ties the weights of the embedding layer and the final
                 # linear layer.
@@ -241,9 +255,11 @@ class GPTBigCodeForCausalLM(nn.Module):
 
             if name == "transformer.wte.weight":
                 # Consider padding in the vocab size.
-                padded_vocab_size = param.shape[0] * tensor_model_parallel_world_size
+                padded_vocab_size = param.shape[
+                    0] * tensor_model_parallel_world_size
                 num_extra_rows = padded_vocab_size - self.config.vocab_size
-                extra_rows = torch.empty(num_extra_rows, loaded_weight.shape[1])
+                extra_rows = torch.empty(num_extra_rows,
+                                         loaded_weight.shape[1])
                 extra_rows = extra_rows.to(loaded_weight)
                 loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
 
@@ -258,25 +274,31 @@ class GPTBigCodeForCausalLM(nn.Module):
                 qkv_array = qkv_array.numpy()
 
                 dims_q = n_head * head_dim
-                q, k, v = np.split(qkv_array, (dims_q, dims_q + head_dim), axis=0)
-                # q is fine, but k & v have not replicated shape along the first axis
-                # as long as MQA is not nativly supported, increase memory and replicated
-                # (head_dim, hidden_dim) to (n_heads * head_dim, hidden_dim)
+                # pylint: disable=unbalanced-tuple-unpacking
+                q, k, v = np.split(qkv_array, (dims_q, dims_q + head_dim),
+                                   axis=0)
+                # q is fine, but k & v have not replicated shape along the first
+                # axis as long as MQA is not nativly supported, increase memory
+                # and replicated (head_dim, hidden_dim) to
+                # (n_heads * head_dim, hidden_dim)
                 if k.ndim == 2 and v.ndim == 2:
                     replication = (n_head, 1)  # weights
                 else:
                     replication = n_head  # biases
                 # replicate n_head times for q, v
                 k, v = np.tile(k, replication), np.tile(v, replication)
-                # concat q, k, v along the first axis (n_heads * head_dim, hidden_dim)
+                # concat q, k, v along the first axis
+                # (n_heads * head_dim, hidden_dim)
                 # to (3 * n_heads * head_dim, hidden_dim)
                 qkv_array = np.concatenate((q, k, v), axis=0)
                 return torch.from_numpy(qkv_array)
 
             # For the fused QKV linear layer, manually shard the weights.
             if "c_attn" in name:
-                # GPT-2's fused QKV has the shape of [3 * num_heads * head_size, hidden_size].
-                # When tensor parallelism is used, we shard the weights along the head dimension.
+                # GPT-2's fused QKV has the shape of
+                # [3 * num_heads * head_size, hidden_size].
+                # When tensor parallelism is used, we shard the weights along
+                # the head dimension.
                 total_num_heads = self.config.num_attention_heads
                 hidden_size = self.config.hidden_size
                 head_size = hidden_size // total_num_heads
@@ -285,13 +307,19 @@ class GPTBigCodeForCausalLM(nn.Module):
                 head_end = (tensor_model_parallel_rank + 1) * num_heads
 
                 if name.endswith(".weight"):
-                    loaded_weight = _expand_mqa_mha(loaded_weight, n_head=total_num_heads, head_dim=head_size)
-                    loaded_weight = loaded_weight.view(3, total_num_heads, head_size, hidden_size)
+                    loaded_weight = _expand_mqa_mha(loaded_weight,
+                                                    n_head=total_num_heads,
+                                                    head_dim=head_size)
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size, hidden_size)
                     loaded_weight = loaded_weight[:, head_start:head_end, :, :]
                     loaded_weight = loaded_weight.reshape(-1, hidden_size)
                 elif name.endswith(".bias"):
-                    loaded_weight = _expand_mqa_mha(loaded_weight, n_head=total_num_heads, head_dim=head_size)
-                    loaded_weight = loaded_weight.view(3, total_num_heads, head_size)
+                    loaded_weight = _expand_mqa_mha(loaded_weight,
+                                                    n_head=total_num_heads,
+                                                    head_dim=head_size)
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size)
                     loaded_weight = loaded_weight[:, head_start:head_end, :]
                     loaded_weight = loaded_weight.reshape(-1)
                 else:

vllm/model_executor/models/gpt_neox.py

@@ -1,5 +1,6 @@
 # coding=utf-8
-# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt_neox/modeling_gpt_neox.py
 # Copyright 2023 The vLLM team.
 # Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved.
 #
@@ -48,19 +49,23 @@ class GPTNeoXAttention(nn.Module):
         self.hidden_size = config.hidden_size
         self.head_size = self.hidden_size // self.total_num_heads
 
-        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
         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.query_key_value = ColumnParallelLinear(config.hidden_size,
-                                                    3 * config.hidden_size,
-                                                    gather_output=False,
-                                                    perform_initialization=False)
-        self.dense = RowParallelLinear(config.hidden_size, config.hidden_size,
+        self.query_key_value = ColumnParallelLinear(
+            config.hidden_size,
+            3 * config.hidden_size,
+            gather_output=False,
+            perform_initialization=False)
+        self.dense = RowParallelLinear(config.hidden_size,
+                                       config.hidden_size,
                                        input_is_parallel=True,
                                        perform_initialization=False)
 
-        scaling = self.head_size ** -0.5
+        scaling = self.head_size**-0.5
         rotary_dim = int(self.head_size * config.rotary_pct)
         assert rotary_dim % 2 == 0
         self.attn = PagedAttentionWithRoPE(self.num_heads, self.head_size,
@@ -78,8 +83,8 @@ class GPTNeoXAttention(nn.Module):
 
         q, k, v = qkv.chunk(chunks=3, dim=-1)
         k_cache, v_cache = kv_cache
-        attn_output = self.attn(
-            position_ids, q, k, v, k_cache, v_cache, input_metadata, cache_event)
+        attn_output = self.attn(position_ids, q, k, v, k_cache, v_cache,
+                                input_metadata, cache_event)
         output, _ = self.dense(attn_output)
         return output
 
@@ -92,7 +97,8 @@ class GPTNeoXMLP(nn.Module):
                                                   config.intermediate_size,
                                                   gather_output=False,
                                                   perform_initialization=False)
-        self.dense_4h_to_h = RowParallelLinear(config.intermediate_size, config.hidden_size,
+        self.dense_4h_to_h = RowParallelLinear(config.intermediate_size,
+                                               config.hidden_size,
                                                input_is_parallel=True,
                                                perform_initialization=False)
         self.act = get_act_fn(config.hidden_act)
@@ -109,8 +115,10 @@ class GPTNeoXLayer(nn.Module):
     def __init__(self, config: GPTNeoXConfig):
         super().__init__()
         self.use_parallel_residual = config.use_parallel_residual
-        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.layer_norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
+                                                     eps=config.layer_norm_eps)
         self.attention = GPTNeoXAttention(config)
         self.mlp = GPTNeoXMLP(config)
 
@@ -154,10 +162,13 @@ class GPTNeoXModel(nn.Module):
         super().__init__()
         self.config = config
 
-        self.embed_in = VocabParallelEmbedding(config.vocab_size, config.hidden_size,
+        self.embed_in = VocabParallelEmbedding(config.vocab_size,
+                                               config.hidden_size,
                                                perform_initialization=False)
-        self.layers = nn.ModuleList([GPTNeoXLayer(config) for _ in range(config.num_hidden_layers)])
-        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.layers = nn.ModuleList(
+            [GPTNeoXLayer(config) for _ in range(config.num_hidden_layers)])
+        self.final_layer_norm = nn.LayerNorm(config.hidden_size,
+                                             eps=config.layer_norm_eps)
 
     def forward(
         self,
@@ -191,8 +202,10 @@ class GPTNeoXForCausalLM(nn.Module):
         super().__init__()
         self.config = config
         self.gpt_neox = GPTNeoXModel(config)
-        self.embed_out = ColumnParallelLinear(config.hidden_size, config.vocab_size,
-                                              bias=False, gather_output=False,
+        self.embed_out = ColumnParallelLinear(config.hidden_size,
+                                              config.vocab_size,
+                                              bias=False,
+                                              gather_output=False,
                                               perform_initialization=False)
         self.sampler = Sampler(config.vocab_size)
 
@@ -204,24 +217,28 @@ class GPTNeoXForCausalLM(nn.Module):
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
     ) -> Dict[int, SequenceOutputs]:
-        hidden_states = self.gpt_neox(
-            input_ids, positions, kv_caches, input_metadata, cache_events)
-        next_tokens = self.sampler(
-            self.embed_out.weight, hidden_states, input_metadata)
+        hidden_states = self.gpt_neox(input_ids, positions, kv_caches,
+                                      input_metadata, cache_events)
+        next_tokens = self.sampler(self.embed_out.weight, hidden_states,
+                                   input_metadata)
         return next_tokens
 
-    _column_parallel_weights = ["embed_in.weight", "embed_out.weight", "dense_h_to_4h.weight", "dense_h_to_4h.bias"]
+    _column_parallel_weights = [
+        "embed_in.weight", "embed_out.weight", "dense_h_to_4h.weight",
+        "dense_h_to_4h.bias"
+    ]
     _row_parallel_weights = ["dense.weight", "dense_4h_to_h.weight"]
 
-    def load_weights(self, model_name_or_path: str,
+    def load_weights(self,
+                     model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      use_np_cache: bool = False):
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
         for name, loaded_weight in hf_model_weights_iterator(
-            model_name_or_path, cache_dir, use_np_cache):
+                model_name_or_path, cache_dir, use_np_cache):
             if ("attention.bias" in name or "attention.masked_bias" in name
-                or "rotary_emb.inv_freq" in name):
+                    or "rotary_emb.inv_freq" in name):
                 continue
             param = state_dict[name]
             if "query_key_value" in name:
@@ -230,17 +247,19 @@ class GPTNeoXForCausalLM(nn.Module):
                 # required shape is [3 * num_heads * head_size, hidden_size].
                 # Thus, we need weight conversion.
                 shard_size = param.shape[0]
-                loaded_weight = loaded_weight[shard_size * tensor_model_parallel_rank
-                                              :shard_size * (tensor_model_parallel_rank + 1)]
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
 
                 num_heads = self.config.num_attention_heads
                 hidden_size = self.config.hidden_size
                 head_size = hidden_size // num_heads
-                if 'query_key_value.weight' in name:
-                    loaded_weight = loaded_weight.view(-1, 3, head_size, hidden_size)
+                if "query_key_value.weight" in name:
+                    loaded_weight = loaded_weight.view(-1, 3, head_size,
+                                                       hidden_size)
                     loaded_weight = loaded_weight.transpose(0, 1)
                     loaded_weight = loaded_weight.reshape(-1, hidden_size)
-                elif 'query_key_value.bias' in name:
+                elif "query_key_value.bias" in name:
                     loaded_weight = loaded_weight.view(-1, 3, head_size)
                     loaded_weight = loaded_weight.transpose(0, 1)
                     loaded_weight = loaded_weight.reshape(-1)

vllm/model_executor/models/llama.py

@@ -1,5 +1,6 @@
 # coding=utf-8
-# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
 # Copyright 2023 The vLLM team.
 # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
 #
@@ -30,7 +31,6 @@ import torch
 from torch import nn
 from transformers import LlamaConfig
 
-from vllm.sequence import SequenceOutputs
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -56,15 +56,19 @@ class LlamaMLP(nn.Module):
         hidden_act: str,
     ):
         super().__init__()
-        self.gate_up_proj = ColumnParallelLinear(hidden_size, 2 * intermediate_size,
-                                                 bias=False, gather_output=False,
+        self.gate_up_proj = ColumnParallelLinear(hidden_size,
+                                                 2 * intermediate_size,
+                                                 bias=False,
+                                                 gather_output=False,
                                                  perform_initialization=False)
-        self.down_proj = RowParallelLinear(intermediate_size, hidden_size,
-                                           bias=False, input_is_parallel=True,
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           input_is_parallel=True,
                                            perform_initialization=False)
-        if hidden_act != 'silu':
-            raise ValueError(f'Unsupported activation: {hidden_act}. '
-                             'Only silu is supported for now.')
+        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):
@@ -83,12 +87,14 @@ class LlamaAttention(nn.Module):
     ):
         super().__init__()
         self.hidden_size = 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 = num_heads
         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 = hidden_size // self.total_num_heads
-        self.scaling = self.head_dim ** -0.5
+        self.scaling = self.head_dim**-0.5
 
         self.qkv_proj = ColumnParallelLinear(
             hidden_size,
@@ -104,8 +110,10 @@ class LlamaAttention(nn.Module):
             input_is_parallel=True,
             perform_initialization=False,
         )
-        self.attn = PagedAttentionWithRoPE(self.num_heads, self.head_dim,
-                                           self.scaling, rotary_dim=self.head_dim)
+        self.attn = PagedAttentionWithRoPE(self.num_heads,
+                                           self.head_dim,
+                                           self.scaling,
+                                           rotary_dim=self.head_dim)
 
     def forward(
         self,
@@ -118,8 +126,8 @@ class LlamaAttention(nn.Module):
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.chunk(chunks=3, dim=-1)
         k_cache, v_cache = kv_cache
-        attn_output = self.attn(
-            positions, q, k, v, k_cache, v_cache, input_metadata, cache_event)
+        attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
+                                input_metadata, cache_event)
         output, _ = self.o_proj(attn_output)
         return output
 
@@ -138,8 +146,10 @@ class LlamaDecoderLayer(nn.Module):
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
         )
-        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)
+        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,
@@ -177,9 +187,13 @@ class LlamaModel(nn.Module):
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
-        self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size,
-                                                   perform_initialization=False)
-        self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            perform_initialization=False)
+        self.layers = nn.ModuleList([
+            LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)
+        ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
     def forward(
@@ -209,6 +223,7 @@ class LlamaModel(nn.Module):
 
 
 class LlamaForCausalLM(nn.Module):
+
     def __init__(self, config):
         super().__init__()
         self.config = config
@@ -228,39 +243,42 @@ class LlamaForCausalLM(nn.Module):
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
     ) -> Dict[int, SequenceOutputs]:
-        hidden_states = self.model(
-            input_ids, positions, kv_caches, input_metadata, cache_events)
-        next_tokens = self.sampler(
-            self.lm_head.weight, hidden_states, input_metadata)
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata, cache_events)
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   input_metadata)
         return next_tokens
 
-    _column_parallel_weights = ["embed_tokens.weight", "lm_head.weight",
-                                "qkv_proj.weight", "gate_proj.weight",
-                                "up_proj.weight"]
+    _column_parallel_weights = [
+        "embed_tokens.weight", "lm_head.weight", "qkv_proj.weight",
+        "gate_proj.weight", "up_proj.weight"
+    ]
     _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
 
-    def load_weights(self, model_name_or_path: str,
+    def load_weights(self,
+                     model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      use_np_cache: bool = False):
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-            model_name_or_path, cache_dir, use_np_cache):
+                model_name_or_path, cache_dir, use_np_cache):
             if "rotary_emb.inv_freq" in name:
                 continue
 
             is_attention_weight = False
-            for stride_id, att_weight_name in enumerate(["q_proj", "k_proj", "v_proj"]):
+            for stride_id, att_weight_name in enumerate(
+                ["q_proj", "k_proj", "v_proj"]):
                 if att_weight_name not in name:
                     continue
                 param = state_dict[name.replace(att_weight_name, "qkv_proj")]
                 shard_size = param.shape[0] // 3
                 loaded_weight = loaded_weight[
-                    shard_size * tensor_model_parallel_rank
-                    :shard_size * (tensor_model_parallel_rank + 1)]
-                param_slice = param.data[shard_size * stride_id
-                                         :shard_size * (stride_id + 1)]
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
                 assert param_slice.shape == loaded_weight.shape
                 param_slice.copy_(loaded_weight)
                 is_attention_weight = True
@@ -275,10 +293,10 @@ class LlamaForCausalLM(nn.Module):
                 param = state_dict[name.replace(weight_name, "gate_up_proj")]
                 shard_size = param.shape[0] // 2
                 loaded_weight = loaded_weight[
-                    shard_size * tensor_model_parallel_rank
-                    :shard_size * (tensor_model_parallel_rank + 1)]
-                param_slice = param.data[shard_size * stride_id
-                                         :shard_size * (stride_id + 1)]
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
                 assert param_slice.shape == loaded_weight.shape
                 param_slice.copy_(loaded_weight)
                 is_gate_up_weight = True

vllm/model_executor/models/opt.py

@@ -1,7 +1,9 @@
 # coding=utf-8
-# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/opt/modeling_opt.py
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/opt/modeling_opt.py
 # Copyright 2023 The vLLM team.
-# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights
+# reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -43,8 +45,9 @@ KVCache = Tuple[torch.Tensor, torch.Tensor]
 class OPTLearnedPositionalEmbedding(nn.Embedding):
 
     def __init__(self, num_embeddings: int, embedding_dim: int):
-        # OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
-        # and adjust num_embeddings appropriately. Other models don't have this hack
+        # OPT is set up so that if padding_idx is specified then offset the
+        # embedding ids by 2 and adjust num_embeddings appropriately. Other
+        # models don't have this hack
         self.offset = 2
         super().__init__(num_embeddings + self.offset, embedding_dim)
 
@@ -62,20 +65,26 @@ class OPTAttention(nn.Module):
     ) -> None:
         super().__init__()
         self.embed_dim = embed_dim
-        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
         total_num_heads = num_heads
         assert num_heads % tensor_model_parallel_world_size == 0
         self.num_heads = total_num_heads // tensor_model_parallel_world_size
         self.head_dim = embed_dim // total_num_heads
-        self.scaling = self.head_dim ** -0.5
+        self.scaling = self.head_dim**-0.5
 
-        self.qkv_proj = ColumnParallelLinear(embed_dim, 3 * embed_dim, bias=bias,
+        self.qkv_proj = ColumnParallelLinear(embed_dim,
+                                             3 * embed_dim,
+                                             bias=bias,
                                              gather_output=False,
                                              perform_initialization=False)
-        self.out_proj = RowParallelLinear(embed_dim, embed_dim, bias=bias,
+        self.out_proj = RowParallelLinear(embed_dim,
+                                          embed_dim,
+                                          bias=bias,
                                           input_is_parallel=True,
                                           perform_initialization=False)
-        self.attn = PagedAttention(self.num_heads, self.head_dim,
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
                                    scale=self.scaling)
 
     def forward(
@@ -88,8 +97,8 @@ class OPTAttention(nn.Module):
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.chunk(chunks=3, dim=-1)
         key_cache, value_cache = kv_cache
-        attn_output = self.attn(
-            q, k, v, key_cache, value_cache, input_metadata, cache_event)
+        attn_output = self.attn(q, k, v, key_cache, value_cache,
+                                input_metadata, cache_event)
         output, _ = self.out_proj(attn_output)
         return output
 
@@ -109,17 +118,21 @@ class OPTDecoderLayer(nn.Module):
         self.activation_fn = get_act_fn(config.activation_function)
 
         self.self_attn_layer_norm = nn.LayerNorm(
-            self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine)
-        self.fc1 = ColumnParallelLinear(self.embed_dim, config.ffn_dim,
+            self.embed_dim,
+            elementwise_affine=config.layer_norm_elementwise_affine)
+        self.fc1 = ColumnParallelLinear(self.embed_dim,
+                                        config.ffn_dim,
                                         bias=config.enable_bias,
                                         gather_output=False,
                                         perform_initialization=False)
-        self.fc2 = RowParallelLinear(config.ffn_dim, self.embed_dim,
+        self.fc2 = RowParallelLinear(config.ffn_dim,
+                                     self.embed_dim,
                                      bias=config.enable_bias,
                                      input_is_parallel=True,
                                      perform_initialization=False)
         self.final_layer_norm = nn.LayerNorm(
-            self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine)
+            self.embed_dim,
+            elementwise_affine=config.layer_norm_elementwise_affine)
 
     def forward(
         self,
@@ -133,11 +146,10 @@ class OPTDecoderLayer(nn.Module):
         # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
         if self.do_layer_norm_before:
             hidden_states = self.self_attn_layer_norm(hidden_states)
-        hidden_states = self.self_attn(
-            hidden_states=hidden_states,
-            kv_cache=kv_cache,
-            input_metadata=input_metadata,
-            cache_event=cache_event)
+        hidden_states = self.self_attn(hidden_states=hidden_states,
+                                       kv_cache=kv_cache,
+                                       input_metadata=input_metadata,
+                                       cache_event=cache_event)
         hidden_states = residual + hidden_states
         # 350m applies layer norm AFTER attention
         if not self.do_layer_norm_before:
@@ -167,35 +179,42 @@ class OPTDecoder(nn.Module):
         self.max_target_positions = config.max_position_embeddings
         self.vocab_size = config.vocab_size
 
-        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
-                                                   config.word_embed_proj_dim,
-                                                   perform_initialization=False)
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.word_embed_proj_dim,
+            perform_initialization=False)
         # Positional embeddings are replicated (not sharded).
         self.embed_positions = OPTLearnedPositionalEmbedding(
             config.max_position_embeddings, config.hidden_size)
 
         # Project out & in will be replicated if they exist.
         if config.word_embed_proj_dim != config.hidden_size:
-            self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False)
+            self.project_out = nn.Linear(config.hidden_size,
+                                         config.word_embed_proj_dim,
+                                         bias=False)
         else:
             self.project_out = None
 
         if config.word_embed_proj_dim != config.hidden_size:
-            self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False)
+            self.project_in = nn.Linear(config.word_embed_proj_dim,
+                                        config.hidden_size,
+                                        bias=False)
         else:
             self.project_in = None
 
-        # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
-        # with checkpoints that have been fine-tuned before transformers v4.20.1
+        # Note that the only purpose of `config._remove_final_layer_norm` is to
+        # keep backward compatibility with checkpoints that have been fine-tuned
+        # before transformers v4.20.1
         # see https://github.com/facebookresearch/metaseq/pull/164
         if config.do_layer_norm_before and not config._remove_final_layer_norm:
             self.final_layer_norm = nn.LayerNorm(
-                config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine
-            )
+                config.hidden_size,
+                elementwise_affine=config.layer_norm_elementwise_affine)
         else:
             self.final_layer_norm = None
 
-        self.layers = nn.ModuleList([OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.layers = nn.ModuleList(
+            [OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])
 
     def forward(
         self,
@@ -217,8 +236,8 @@ class OPTDecoder(nn.Module):
             else:
                 cache_event = cache_events[i]
             layer = self.layers[i]
-            hidden_states = layer(
-                hidden_states, kv_caches[i], input_metadata, cache_event)
+            hidden_states = layer(hidden_states, kv_caches[i], input_metadata,
+                                  cache_event)
 
         if self.final_layer_norm is not None:
             hidden_states = self.final_layer_norm(hidden_states)
@@ -241,8 +260,8 @@ class OPTModel(nn.Module):
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
     ) -> torch.Tensor:
-        return self.decoder(
-            input_ids, positions, kv_caches, input_metadata, cache_events)
+        return self.decoder(input_ids, positions, kv_caches, input_metadata,
+                            cache_events)
 
 
 class OPTForCausalLM(nn.Module):
@@ -264,23 +283,26 @@ class OPTForCausalLM(nn.Module):
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
     ) -> Dict[int, SequenceOutputs]:
-        hidden_states = self.model(
-            input_ids, positions, kv_caches, input_metadata, cache_events)
-        next_tokens = self.sampler(
-            self.lm_head_weight, hidden_states, input_metadata)
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata, cache_events)
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   input_metadata)
         return next_tokens
 
-    _column_parallel_weights = ["embed_tokens.weight", "fc1.weight", "fc1.bias"]
+    _column_parallel_weights = [
+        "embed_tokens.weight", "fc1.weight", "fc1.bias"
+    ]
     _row_parallel_weights = ["out_proj.weight", "fc2.weight"]
 
-    def load_weights(self, model_name_or_path: str,
+    def load_weights(self,
+                     model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      use_np_cache: bool = False):
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-            model_name_or_path, cache_dir, use_np_cache):
+                model_name_or_path, cache_dir, use_np_cache):
             if "lm_head.weight" in name:
                 continue
 
@@ -288,16 +310,17 @@ class OPTForCausalLM(nn.Module):
                 name = "model." + name
 
             is_attention_weight = False
-            for stride_id, att_weight_name in enumerate(["q_proj", "k_proj", "v_proj"]):
+            for stride_id, att_weight_name in enumerate(
+                ["q_proj", "k_proj", "v_proj"]):
                 if att_weight_name not in name:
                     continue
                 param = state_dict[name.replace(att_weight_name, "qkv_proj")]
                 shard_size = param.shape[0] // 3
                 loaded_weight = loaded_weight[
-                    shard_size * tensor_model_parallel_rank
-                    :shard_size * (tensor_model_parallel_rank + 1)]
-                param_slice = param.data[shard_size * stride_id
-                                         :shard_size * (stride_id + 1)]
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
                 assert param_slice.shape == loaded_weight.shape
                 param_slice.copy_(loaded_weight)
                 is_attention_weight = True

vllm/model_executor/weight_utils.py

@@ -44,9 +44,9 @@ def hf_model_weights_iterator(
     if use_np_cache:
         # Convert the model weights from torch tensors to numpy arrays for
         # faster loading.
-        np_folder = os.path.join(hf_folder, 'np')
+        np_folder = os.path.join(hf_folder, "np")
         os.makedirs(np_folder, exist_ok=True)
-        weight_names_file = os.path.join(np_folder, 'weight_names.json')
+        weight_names_file = os.path.join(np_folder, "weight_names.json")
         with lock:
             if not os.path.exists(weight_names_file):
                 weight_names = []
@@ -57,10 +57,10 @@ def hf_model_weights_iterator(
                         with open(param_path, "wb") as f:
                             np.save(f, param.cpu().detach().numpy())
                         weight_names.append(name)
-                with open(weight_names_file, 'w') as f:
+                with open(weight_names_file, "w") as f:
                     json.dump(weight_names, f)
 
-        with open(weight_names_file, 'r') as f:
+        with open(weight_names_file, "r") as f:
             weight_names = json.load(f)
 
         for name in weight_names:
@@ -86,17 +86,16 @@ def load_tensor_parallel_weights(
     for p in column_parallel_weight_names:
         if p in param_name:
             shard_size = param.shape[0]
-            loaded_weight = loaded_weight[
-                shard_size * tensor_model_parallel_rank
-                :shard_size * (tensor_model_parallel_rank + 1)]
+            start_idx = tensor_model_parallel_rank * shard_size
+            end_idx = (tensor_model_parallel_rank + 1) * shard_size
+            loaded_weight = loaded_weight[start_idx:end_idx]
             break
     for p in row_parallel_weight_names:
         if p in param_name:
             shard_size = param.shape[1]
-            loaded_weight = loaded_weight[
-                :,
-                shard_size * tensor_model_parallel_rank
-                :shard_size * (tensor_model_parallel_rank + 1)]
+            start_idx = tensor_model_parallel_rank * shard_size
+            end_idx = (tensor_model_parallel_rank + 1) * shard_size
+            loaded_weight = loaded_weight[:, start_idx:end_idx]
             break
     assert param.shape == loaded_weight.shape, (
         f"{param_name} shape mismatch between model and checkpoint: "