Support beam search & parallel generation (#7)

cacheflow/block.py

@@ -35,6 +35,10 @@ class LogicalTokenBlock:
     def get_token_ids(self) -> List[int]:
         return self.token_ids[:self.num_tokens]
 
+    def get_last_token_id(self) -> int:
+        assert self.num_tokens > 0
+        return self.token_ids[self.num_tokens - 1]
+
 
 class PhysicalTokenBlock:
 

cacheflow/master/frontend.py

@@ -1,4 +1,4 @@
-from typing import List, Optional, Tuple
+from typing import List, Optional, Set, Tuple
 
 from transformers import AutoTokenizer
 
@@ -25,12 +25,35 @@ class Frontend:
     def query(
         self,
         prompt: str,
-        sampling_params: Optional[SamplingParams] = None,
+        n: int = 1,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        use_beam_search: bool = False,
+        stop_token_ids: Set[int] = set(),
+        max_num_steps: int = 16,  # From OpenAI API.
+        num_logprobs: int = 0,
+        context_window_size: Optional[int] = None,
     ) -> None:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        token_ids: List[int] = self.tokenizer.encode(prompt)
+        # Stop when we see an EOS token.
+        stop_token_ids.add(self.tokenizer.eos_token_id)
+        sampling_params = SamplingParams(
+            n=n,
+            temperature=temperature,
+            top_p=top_p,
+            use_beam_search=use_beam_search,
+            stop_token_ids=stop_token_ids,
+            max_num_steps=max_num_steps,
+            num_logprobs=num_logprobs,
+            context_window_size=context_window_size,
+        )
+        token_ids = self.tokenizer.encode(prompt)
+        self._add_query(token_ids, sampling_params)
 
+    def _add_query(
+        self,
+        token_ids: List[int],
+        sampling_params: SamplingParams,
+    ) -> None:
         seqs: List[Sequence] = []
         for _ in range(sampling_params.n):
             seq_id = next(self.seq_counter)

cacheflow/master/scheduler.py

@@ -1,10 +1,12 @@
-from typing import Dict, List, Tuple
+from typing import Dict, List
 
 from cacheflow.master.block_manager import BlockSpaceManager
 from cacheflow.master.frontend import Frontend
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.sequence import Sequence
 from cacheflow.sequence import SequenceGroup
+from cacheflow.sequence import SequenceGroupInputs
+from cacheflow.sequence import SequenceOutputs
 from cacheflow.sequence import SequenceStatus
 
 _MAX_NUM_BATCHED_TOKENS = 2048
@@ -66,7 +68,7 @@ class Scheduler:
     def _append(
         self,
         seq_group: SequenceGroup,
-        blocks_to_copy: Dict[int, int],
+        blocks_to_copy: Dict[int, List[int]],
     ) -> None:
         for seq in seq_group.seqs:
             if seq.status == SequenceStatus.FINISHED:
@@ -74,7 +76,10 @@ class Scheduler:
             ret = self.block_manager.append(seq)
             if ret is not None:
                 src_block, dst_block = ret
-                blocks_to_copy[src_block] = dst_block
+                if src_block in blocks_to_copy:
+                    blocks_to_copy[src_block].append(dst_block)
+                else:
+                    blocks_to_copy[src_block] = [dst_block]
 
     def _swap_in(
         self,
@@ -83,9 +88,8 @@ class Scheduler:
     ) -> None:
         mapping = self.block_manager.swap_in(seq_group)
         blocks_to_swap_in.update(mapping)
-        for seq in seq_group.seqs:
-            if seq.status == SequenceStatus.SWAPPED:
-                seq.status = SequenceStatus.RUNNING
+        for seq in seq_group.get_seqs(status=SequenceStatus.SWAPPED):
+            seq.status = SequenceStatus.RUNNING
         self.running.append(seq_group)
 
     def _swap_out(
@@ -96,16 +100,15 @@ class Scheduler:
         assert self.block_manager.can_swap_out(seq_group)
         mapping = self.block_manager.swap_out(seq_group)
         blocks_to_swap_out.update(mapping)
-        for seq in seq_group.seqs:
-            if seq.status == SequenceStatus.RUNNING:
-                seq.status = SequenceStatus.SWAPPED
+        for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
+            seq.status = SequenceStatus.SWAPPED
         self.swapped.append(seq_group)
 
     def step(self) -> None:
         # Blocks that need to be swaped or copied before model execution.
         blocks_to_swap_in: Dict[int, int] = {}
         blocks_to_swap_out: Dict[int, int] = {}
-        blocks_to_copy: Dict[int, int] = {}
+        blocks_to_copy: Dict[int, List[int]] = {}
 
         # 1. Reserve new slots for the running sequences.
         # NOTE: Here we implicitly assume FCFS scheduling.
@@ -143,6 +146,10 @@ class Scheduler:
             # All swapped sequences are swapped in.
             self.swapped.clear()
 
+        # Ensure that swap-in and swap-out never happen at the same timestep.
+        if blocks_to_swap_in:
+            assert not blocks_to_swap_out
+
         num_batched_tokens = sum(
             seq_group.num_seqs(status=SequenceStatus.RUNNING)
             for seq_group in self.running
@@ -152,7 +159,6 @@ class Scheduler:
         # NOTE: Here we implicitly assume FCFS scheduling.
         # TODO(woosuk): Add a batching policy to control the batch size.
         if not self.swapped:
-            # FIXME(woosuk): Acquire a lock to protect pending.
             self._fetch_inputs()
             for i, seq_group in enumerate(self.pending):
                 num_prompt_tokens = seq_group.seqs[0].get_len()
@@ -168,39 +174,45 @@ class Scheduler:
             else:
                 self.pending.clear()
 
-        # Ensure that swap-in and swap-out never happen at the same timestep.
-        if blocks_to_swap_in:
-            assert not blocks_to_swap_out
-
         # 4. Create input data structures.
-        prompt_tokens: Dict[int, List[int]] = {}
-        generation_tokens: Dict[int, int] = {}
-        context_lens: Dict[int, int] = {}
-        block_tables: Dict[int, List[int]] = {}
+        input_seq_groups: List[SequenceGroupInputs] = []
         for seq_group in self.running:
             group_id = seq_group.group_id
             num_steps = self.num_steps[group_id]
+
             # NOTE(woosuk): We assume that the number of steps is 0
             # for the prompt sequences.
             is_prompt = num_steps == 0
-            for seq in seq_group.seqs:
-                if seq.status != SequenceStatus.RUNNING:
-                    continue
 
+            input_tokens: Dict[int, List[int]] = {}
+            seq_logprobs: Dict[int, float] = {}
+            block_tables: Dict[int, List[int]] = {}
+            for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
                 seq_id = seq.seq_id
                 block_tables[seq_id] = self.block_manager.get_block_table(seq)
                 if is_prompt:
-                    prompt_tokens[seq_id] = seq.get_token_ids()
+                    input_tokens[seq_id] = seq.get_token_ids()
                 else:
-                    generation_tokens[seq_id] = seq.get_token_ids()[-1]
-                    context_lens[seq_id] = seq.get_len()
+                    input_tokens[seq_id] = [seq.get_last_token_id()]
+                seq_logprobs[seq_id] = seq.cumulative_logprobs
+                # NOTE(woosuk): Sequences in the same group have the same
+                # sequence length
+                seq_len = seq.get_len()
+
+            input_seq_group = SequenceGroupInputs(
+                group_id=group_id,
+                is_prompt=is_prompt,
+                input_tokens=input_tokens,
+                context_len=seq_len,
+                seq_logprobs=seq_logprobs,
+                sampling_params=self.sampling_params[group_id],
+                block_tables=block_tables,
+            )
+            input_seq_groups.append(input_seq_group)
 
         # 5. Execute the first stage of the pipeline.
         self.controllers[0].execute_stage(
-            prompt_tokens,
-            generation_tokens,
-            context_lens,
-            block_tables,
+            input_seq_groups,
             blocks_to_swap_in,
             blocks_to_swap_out,
             blocks_to_copy,
@@ -208,7 +220,7 @@ class Scheduler:
 
     def post_step(
         self,
-        next_tokens: Dict[int, Tuple[int, int]],
+        seq_outputs: Dict[int, SequenceOutputs],
     ) -> None:
         # Update the running sequences and free blocks.
         for seq_group in self.running:
@@ -216,25 +228,32 @@ class Scheduler:
             self.num_steps[group_id] += 1
             stop_token_ids = self.sampling_params[group_id].stop_token_ids
 
+            # Process beam search results before processing the next tokens.
             for seq in seq_group.seqs:
                 if seq.status == SequenceStatus.FINISHED:
                     continue
 
-                parent_seq_id, next_token = next_tokens[seq.seq_id]
-                if seq.seq_id != parent_seq_id:
+                output = seq_outputs[seq.seq_id]
+                if seq.seq_id != output.parent_seq_id:
                     # The sequence is a fork of the parent sequence (beam search).
                     # Free the current sequence.
                     self.block_manager.free(seq)
                     # Fork the parent sequence.
-                    parent_seq = seq_group.find(parent_seq_id)
-                    seq.logical_token_blocks = parent_seq.logical_token_blocks.copy()
+                    parent_seq = seq_group.find(output.parent_seq_id)
+                    parent_seq.fork(seq)
                     self.block_manager.fork(parent_seq, seq)
 
+            # Process the next tokens.
+            for seq in seq_group.seqs:
+                if seq.status == SequenceStatus.FINISHED:
+                    continue
+
                 # Append a new token to the sequence.
-                seq.append([next_token])
+                output = seq_outputs[seq.seq_id]
+                seq.append(output.output_token, output.logprobs)
 
                 # Check if the sequence has generated a stop token.
-                if next_token in stop_token_ids:
+                if output.output_token in stop_token_ids:
                     self._free_seq(seq)
                     continue
 

cacheflow/models/__init__.py

@@ -1,8 +1,10 @@
 from cacheflow.models.input_metadata import InputMetadata
 from cacheflow.models.model_utils import get_model
+from cacheflow.models.model_utils import set_seed
 
 
 __all__ = [
-    'get_model',
     'InputMetadata',
+    'get_model',
+    'set_seed'
 ]

cacheflow/models/input_metadata.py

@@ -1,21 +1,24 @@
-from typing import List
+from typing import List, Dict, Tuple
 
 import torch
 
+from cacheflow.sampling_params import SamplingParams
+
 
 class InputMetadata:
 
     def __init__(
         self,
-        seq_ids: List[int],
+        seq_groups: List[Tuple[List[int], SamplingParams]],
+        seq_logprobs: Dict[int, float],                         # Seq id -> cumulative logprobs.
         prompt_lens: List[int],
         slot_mapping: torch.Tensor,
         context_lens: torch.Tensor,
-        # FIXME: Rename
         max_context_len: int,
         block_tables: torch.Tensor,
     ) -> None:
-        self.seq_ids = seq_ids
+        self.seq_groups = seq_groups
+        self.seq_logprobs = seq_logprobs
         self.prompt_lens = prompt_lens
         self.slot_mapping = slot_mapping
         self.context_lens = context_lens
@@ -23,19 +26,20 @@ class InputMetadata:
         self.block_tables = block_tables
 
         self.num_prompts = len(prompt_lens)
+        self.num_prompt_tokens = sum(prompt_lens)
         self.num_generation_tokens = context_lens.shape[0]
         self.num_valid_tokens = slot_mapping.shape[0]
         if block_tables.numel() > 0:
             self.max_num_blocks_per_seq = block_tables.shape[1]
         else:
             self.max_num_blocks_per_seq = 0
-        assert self.num_generation_tokens == block_tables.shape[0]
-        assert self.num_prompts + self.num_generation_tokens == len(seq_ids)
+        assert block_tables.shape[0] == self.num_generation_tokens
+        assert context_lens.shape[0] == self.num_generation_tokens
 
     def __repr__(self) -> str:
         return (f'InputMetadata('
-                f'seq_ids={self.seq_ids}, '
                 f'num_prompts={self.num_prompts}, '
+                f'num_prompt_tokens={self.num_prompt_tokens}, '
                 f'num_generation_tokens={self.num_generation_tokens}, '
                 f'num_valid_tokens={self.num_valid_tokens}, '
                 f'max_num_blocks_per_seq={self.max_num_blocks_per_seq}, '

cacheflow/models/model_utils.py

@@ -1,5 +1,7 @@
+import random
 from typing import Union
 
+import numpy as np
 import torch
 import torch.nn as nn
 
@@ -30,3 +32,11 @@ def get_model(
             model = hf_model.from_pretrained(model_name, torch_dtype=torch_dtype)
             return model.eval()
     raise ValueError(f'Invalid model name: {model_name}')
+
+
+def set_seed(seed: int) -> None:
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)

cacheflow/models/opt.py

@@ -9,6 +9,7 @@ from transformers import PreTrainedModel
 from cacheflow.models import InputMetadata
 from cacheflow.models.attention import OPTCacheFlowAttention
 from cacheflow.models.sample import Sampler
+from cacheflow.sequence import SequenceOutputs
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -261,7 +262,7 @@ class OPTForCausalLM(OPTPreTrainedModel):
         kv_caches: List[KVCache],
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
-    ) -> Dict[int, Tuple[int, int]]:
+    ) -> Dict[int, SequenceOutputs]:
         hidden_states = self.model(
             input_ids, positions, kv_caches, input_metadata, cache_events)
         next_tokens = self.sampler(

cacheflow/models/sample.py

@@ -4,6 +4,8 @@ import torch
 import torch.nn as nn
 
 from cacheflow.models import InputMetadata
+from cacheflow.sampling_params import SamplingParams
+from cacheflow.sequence import SequenceOutputs
 
 
 class Sampler(nn.Module):
@@ -16,27 +18,266 @@ class Sampler(nn.Module):
         embedding: torch.Tensor,
         hidden_states: torch.Tensor,
         input_metadata: InputMetadata,
-    ) -> Dict[int, Tuple[int, int]]:
-        # Get the hidden states of the last tokens.
-        start_idx = 0
-        last_token_indicies: List[int] = []
-        for prompt_len in input_metadata.prompt_lens:
-            last_token_indicies.append(start_idx + prompt_len - 1)
-            start_idx += prompt_len
-        last_token_indicies.extend(
-            range(start_idx, start_idx + input_metadata.num_generation_tokens))
-        hidden_states = hidden_states[last_token_indicies]
+    ) -> Dict[int, SequenceOutputs]:
+        # Get the hidden states that we use for sampling.
+        hidden_states = _prune_hidden_states(hidden_states, input_metadata)
 
         # Get the logits for the next tokens.
         logits = torch.matmul(hidden_states, embedding.t())
 
-        # Sample the next tokens.
-        # TODO(woosuk): Implement other sampling methods.
-        next_token_ids = torch.argmax(logits, dim=-1)
-        next_token_ids = next_token_ids.tolist()
+        # 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)
+            # Use in-place division to avoid creating a new tensor.
+            logits.div_(t.unsqueeze(dim=1))
 
-        # Return the next tokens.
-        next_tokens: Dict[int, Tuple[int, int]] = {}
-        for seq_id, token_id in zip(input_metadata.seq_ids, next_token_ids):
-            next_tokens[seq_id] = (seq_id, token_id)
-        return next_tokens
+        # Compute the probabilities.
+        probs = torch.softmax(logits, dim=-1, dtype=torch.float)
+        # Compute the log probabilities (before applying top-p).
+        logprobs = torch.log(probs)
+
+        # Apply top-p truncation.
+        top_ps = _get_top_ps(input_metadata)
+        assert len(top_ps) == probs.shape[0]
+        if any(p < 1.0 for p in top_ps):
+            p = torch.tensor(top_ps, dtype=probs.dtype, device=probs.device)
+            probs = _apply_top_p(probs, p)
+
+        # Sample the next tokens.
+        return _sample(probs, logprobs, input_metadata)
+
+
+def _prune_hidden_states(
+    hidden_states: torch.Tensor,
+    input_metadata: InputMetadata,
+) -> torch.Tensor:
+    start_idx = 0
+    last_token_indicies: List[int] = []
+    for prompt_len in input_metadata.prompt_lens:
+        last_token_indicies.append(start_idx + prompt_len - 1)
+        start_idx += prompt_len
+    last_token_indicies.extend(
+        range(start_idx, start_idx + input_metadata.num_generation_tokens))
+    return hidden_states[last_token_indicies]
+
+
+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):
+        seq_ids, sampling_params = seq_group
+        temperature = sampling_params.temperature
+        if temperature == 0.0:
+            # NOTE: Zero temperature means deterministic sampling
+            # (i.e., greedy sampling or beam search).
+            # Set the temperature to 1 to avoid division by zero.
+            temperature = 1.0
+
+        if i < input_metadata.num_prompts:
+            # A prompt input.
+            temperatures.append(temperature)
+        else:
+            # A generation token.
+            temperatures += [temperature] * len(seq_ids)
+    return temperatures
+
+
+def _get_top_ps(
+    input_metadata: InputMetadata,
+) -> List[float]:
+    top_ps: List[float] = []
+    for i, seq_group in enumerate(input_metadata.seq_groups):
+        seq_ids, sampling_params = seq_group
+        if i < input_metadata.num_prompts:
+            # A prompt input.
+            top_ps.append(sampling_params.top_p)
+        else:
+            # A generation token.
+            top_ps += [sampling_params.top_p] * len(seq_ids)
+    return top_ps
+
+
+def _apply_top_p(
+    probs: torch.Tensor,
+    p: torch.Tensor,
+) -> torch.Tensor:
+    # TODO(woosuk): Optimize.
+    probs_sort, probs_idx = probs.sort(dim=-1, descending=True)
+    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    mask = (probs_sum - probs_sort) > p.unsqueeze(dim=1)
+    probs_sort[mask] = 0.0
+    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
+    probs = torch.gather(
+        probs_sort, dim=-1, index=torch.argsort(probs_idx, dim=-1))
+    return probs
+
+
+def _get_topk_logprobs(
+    logprobs: torch.Tensor,
+    num_logprobs: int,
+) -> Dict[int, float]:
+    if num_logprobs == 0:
+        return {}
+
+    topk_logprobs, topk_ids = torch.topk(logprobs, num_logprobs)
+    if num_logprobs == 1:
+        topk_logprobs = [topk_logprobs.item()]
+        topk_ids = [topk_ids.item()]
+    else:
+        topk_logprobs = topk_logprobs.tolist()
+        topk_ids = topk_ids.tolist()
+
+    token_to_logprob: Dict[int, float] = {}
+    for token_id, logprob in zip(topk_ids, topk_logprobs):
+        token_to_logprob[token_id] = logprob
+    return token_to_logprob
+
+
+def _sample_from_prompt(
+    prob: torch.Tensor,
+    sampling_params: SamplingParams,
+) -> List[int]:
+    if sampling_params.use_beam_search:
+        # Beam search.
+        beam_width = sampling_params.n
+        _, next_token_ids = torch.topk(prob, beam_width)
+        next_token_ids = next_token_ids.tolist()
+    elif sampling_params.temperature == 0.0:
+        # Greedy sampling.
+        assert sampling_params.n == 1
+        next_token_id = torch.argmax(prob)
+        next_token_ids = [next_token_id.item()]
+    else:
+        # Neucleus sampling.
+        # Sample n tokens for the prompt.
+        n = sampling_params.n
+        next_token_ids = torch.multinomial(
+            prob, num_samples=n, replacement=True)
+        next_token_ids = next_token_ids.tolist()
+    return next_token_ids
+
+
+def _sample_from_generation_tokens(
+    seq_ids: List[int],
+    probs: torch.Tensor,
+    logprobs: torch.Tensor,
+    seq_logprobs: List[float],
+    sampling_params: SamplingParams,
+) -> Tuple[List[int], List[int]]:
+    # NOTE(woosuk): sampling_params.n can be greater than
+    # len(seq_ids) because some sequences in the group might have
+    # been already terminated.
+    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)
+        logprobs = logprobs + seq_logprobs.unsqueeze(dim=1)
+
+        vocab_size = logprobs.size(-1)
+        beam_width = len(seq_ids)
+        _, topk_ids = torch.topk(logprobs.flatten(), beam_width)
+        seq_idx = torch.div(topk_ids, vocab_size, rounding_mode='floor').tolist()
+        beam_seq_ids = [seq_ids[i] for i in seq_idx]
+        token_ids = (topk_ids % vocab_size).tolist()
+
+        beam_outputs: Dict[int, Tuple[int, int]] = {}
+        outstanding_beams: List[Tuple[int, int]] = []
+        # If a beam survives, continue with it.
+        for seq_id, token_id in zip(beam_seq_ids, token_ids):
+            if seq_id not in beam_outputs:
+                beam_outputs[seq_id] = (seq_id, token_id)
+            else:
+                outstanding_beams.append((seq_id, token_id))
+
+        # If a beam is discarded, fork another beam.
+        for seq_id in seq_ids:
+            if seq_id not in beam_outputs:
+                beam_outputs[seq_id] = outstanding_beams.pop()
+        assert not outstanding_beams
+
+        parent_seq_ids = [beam_outputs[seq_id][0] for seq_id in seq_ids]
+        next_token_ids = [beam_outputs[seq_id][1] for seq_id in seq_ids]
+    elif sampling_params.temperature == 0.0:
+        # Greedy sampling.
+        assert len(seq_ids) == 1
+        next_token_id = torch.argmax(probs, dim=-1)
+        next_token_ids = [next_token_id.item()]
+        parent_seq_ids = seq_ids
+    else:
+        # Neucleus sampling.
+        # Sample 1 token for each sequence in the group.
+        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
+
+
+def _sample(
+    probs: torch.Tensor,
+    logprobs: torch.Tensor,
+    input_metadata: InputMetadata,
+) -> Dict[int, SequenceOutputs]:
+    seq_outputs: Dict[int, SequenceOutputs] = {}
+
+    # TODO(woosuk): Optimize.
+    idx = 0
+    for i, seq_group in enumerate(input_metadata.seq_groups):
+        seq_ids, sampling_params = seq_group
+        if i < input_metadata.num_prompts:
+            # Generate the next tokens for a prompt input.
+            assert len(seq_ids) == sampling_params.n
+            prob = probs[idx]
+            logprob = logprobs[idx]
+            idx += 1
+
+            # 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.num_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)
+        else:
+            # Generate the next tokens for generation tokens.
+            prob = probs[idx:idx + len(seq_ids)]
+            logprob = logprobs[idx:idx + len(seq_ids)]
+            idx += len(seq_ids)
+
+            # Sample the next tokens.
+            seq_logprobs = [
+                input_metadata.seq_logprobs[seq_id] 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):
+                next_logprobs[seq_id] = _get_topk_logprobs(
+                    logprob[i], sampling_params.num_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)
+                output_logprobs = next_logprobs[parent_seq_id].copy()
+                output_logprobs[next_token_id] = logprob[i, next_token_id].item()
+                seq_outputs[seq_id] = SequenceOutputs(
+                    seq_id,
+                    parent_seq_id,
+                    next_token_id,
+                    output_logprobs,
+                )
+
+    return seq_outputs

cacheflow/sampling_params.py

@@ -5,27 +5,51 @@ class SamplingParams:
 
     def __init__(
         self,
-        n: int = 1,
-        temperature: float = 1.0,
-        top_p: float = 1.0,
-        use_beam_search: bool = False,
-        stop_token_ids: Set[int] = [],
-        max_num_steps: int = 16,  # From OpenAI API.
-        max_context_len: Optional[int] = None,
+        n: int,
+        temperature: float,
+        top_p: float,
+        use_beam_search: bool,
+        stop_token_ids: Set[int],
+        max_num_steps: int,
+        num_logprobs: int,
+        context_window_size: Optional[int],
     ) -> None:
-        assert n >= 1
-        assert temperature >= 0.0
-        assert 0.0 < top_p <= 1.0
+        if n < 1:
+            raise ValueError(f'n must be at least 1, got {n}.')
+        if temperature < 0.0:
+            raise ValueError(
+                f'temperature must be non-negative, got {temperature}.')
+        if not 0.0 < top_p <= 1.0:
+            raise ValueError(f'top_p must be in (0, 1], got {top_p}.')
+        if max_num_steps < 1:
+            raise ValueError(
+                f'max_num_steps must be at least 1, got {max_num_steps}.')
+        if num_logprobs < 0:
+            raise ValueError(
+                f'num_logprobs must be non-negative, got {num_logprobs}.')
+        if context_window_size is not None and context_window_size < 0:
+            raise ValueError(
+                'context_window_size must be non-negative, '
+                f'got {context_window_size}.')
+
         if use_beam_search:
-            assert n > 1
-            assert temperature > 0.0
-            assert top_p == 1.0
+            if n == 1:
+                raise ValueError(
+                    'n must be greater than 1 when using beam search.')
+            if temperature > 0.0:
+                raise ValueError(
+                    'temperature must be 0 when using beam search.')
+            if top_p < 1.0:
+                raise ValueError(
+                    'top_p must be 1 when using beam search.')
         elif temperature == 0.0:
-            # Zero temperature means greedy decoding.
-            assert n == 1
-            assert top_p == 1.0
-        assert max_num_steps >= 1
-        assert max_context_len is None or max_context_len >= 0
+            # Zero temperature means greedy sampling.
+            if n > 1:
+                raise ValueError(
+                    'n must be 1 when using greedy sampling.')
+            if top_p < 1.0:
+                raise ValueError(
+                    'top_p must be 1 when using greedy sampling.')
 
         self.n = n
         self.temperature = temperature
@@ -33,4 +57,15 @@ class SamplingParams:
         self.use_beam_search = use_beam_search
         self.stop_token_ids = stop_token_ids
         self.max_num_steps = max_num_steps
-        self.max_context_len = max_context_len
+        self.num_logprobs = num_logprobs
+        self.context_window_size = context_window_size
+
+    def __repr__(self) -> str:
+        return (f'SamplingParams(n={self.n}, '
+                f'temperature={self.temperature}, '
+                f'top_p={self.top_p}, '
+                f'use_beam_search={self.use_beam_search}, '
+                f'stop_token_ids={self.stop_token_ids}, '
+                f'max_num_steps={self.max_num_steps}, '
+                f'num_logprobs={self.num_logprobs}, '
+                f'context_window_size={self.context_window_size})')

cacheflow/sequence.py

@@ -1,7 +1,9 @@
+import copy
 import enum
-from typing import List, Optional
+from typing import Dict, List, Optional
 
 from cacheflow.block import LogicalTokenBlock
+from cacheflow.sampling_params import SamplingParams
 
 
 class SequenceStatus(enum.Enum):
@@ -24,9 +26,11 @@ class Sequence:
 
         self.logical_token_blocks: List[LogicalTokenBlock] = []
         # Initialize the logical token blocks with the given token ids.
-        self.append(token_ids)
+        self.add(token_ids)
 
         self.status = SequenceStatus.PENDING
+        self.output_logprobs: List[Dict[int, float]] = []
+        self.cumulative_logprobs = 1.0
 
     def add_block(self) -> None:
         block = LogicalTokenBlock(
@@ -35,7 +39,7 @@ class Sequence:
         )
         self.logical_token_blocks.append(block)
 
-    def append(self, token_ids: List[int]) -> None:
+    def add(self, token_ids: List[int]) -> None:
         while token_ids:
             if not self.logical_token_blocks:
                 self.add_block()
@@ -49,6 +53,12 @@ class Sequence:
             last_block.append(token_ids[:num_empty_slots])
             token_ids = token_ids[num_empty_slots:]
 
+    def append(self, token_id: int, logprobs: Dict[int, float]) -> None:
+        assert token_id in logprobs
+        self.add([token_id])
+        self.output_logprobs.append(logprobs)
+        self.cumulative_logprobs += logprobs[token_id]
+
     def get_len(self) -> int:
         return sum(block.num_tokens for block in self.logical_token_blocks)
 
@@ -58,6 +68,14 @@ class Sequence:
             token_ids.extend(block.get_token_ids())
         return token_ids
 
+    def get_last_token_id(self) -> int:
+        return self.logical_token_blocks[-1].get_last_token_id()
+
+    def fork(self, child_seq: 'Sequence') -> 'Sequence':
+        child_seq.logical_token_blocks = copy.deepcopy(self.logical_token_blocks)
+        child_seq.output_logprobs = copy.deepcopy(self.output_logprobs)
+        child_seq.cumulative_logprobs = self.cumulative_logprobs
+
     def __repr__(self) -> str:
         return (f'Sequence(seq_id={self.seq_id}, '
                 f'status={self.status.name}, '
@@ -74,11 +92,17 @@ class SequenceGroup:
         self.group_id = group_id
         self.seqs = seqs
 
-    def num_seqs(self, status: Optional[SequenceStatus] = None) -> int:
+    def get_seqs(
+        self,
+        status: Optional[SequenceStatus] = None,
+    ) -> List[Sequence]:
         if status is None:
-            return len(self.seqs)
+            return self.seqs
         else:
-            return len([seq for seq in self.seqs if seq.status == status])
+            return [seq for seq in self.seqs if seq.status == status]
+
+    def num_seqs(self, status: Optional[SequenceStatus] = None) -> int:
+        return len(self.get_seqs(status))
 
     def find(self, seq_id: int) -> Sequence:
         for seq in self.seqs:
@@ -92,3 +116,45 @@ class SequenceGroup:
     def __repr__(self) -> str:
         return (f'SequenceGroup(group_id={self.group_id}, '
                 f'num_seqs={len(self.seqs)})')
+
+
+class SequenceGroupInputs:
+
+    def __init__(
+        self,
+        group_id: int,
+        is_prompt: bool,
+        input_tokens: Dict[int, List[int]],     # Seq id -> token ids.
+        context_len: int,
+        seq_logprobs: Dict[int, float],         # Seq id -> cumulative logprobs.
+        sampling_params: SamplingParams,
+        block_tables: Dict[int, List[int]],     # Seq id -> List of physical block numbers.
+    ) -> None:
+        self.group_id = group_id
+        self.is_prompt = is_prompt
+        self.input_tokens = input_tokens
+        self.context_len = context_len
+        self.seq_logprobs = seq_logprobs
+        self.sampling_params = sampling_params
+        self.block_tables = block_tables
+
+
+class SequenceOutputs:
+
+    def __init__(
+        self,
+        seq_id: int,
+        parent_seq_id: int,
+        output_token: int,
+        logprobs: Dict[int, float],         # Token id -> logP(x_i+1 | x_0, ..., x_i).
+    ) -> None:
+        self.seq_id = seq_id
+        self.parent_seq_id = parent_seq_id
+        self.output_token = output_token
+        self.logprobs = logprobs
+
+    def __repr__(self) -> str:
+        return (f'SequenceOutputs(seq_id={self.seq_id}, '
+                f'parent_seq_id={self.parent_seq_id}, '
+                f'output_token={self.output_token}), '
+                f'logprobs={self.logprobs}')

cacheflow/worker/cache_engine.py

@@ -97,7 +97,7 @@ class CacheEngine:
             cpu_cache.append((key_blocks, value_blocks))
         return cpu_cache
 
-    def _copy_blocks(
+    def _swap(
         self,
         src: List[KVCache],
         dst: List[KVCache],
@@ -108,19 +108,38 @@ class CacheEngine:
                 src_key_cache, src_value_cache = src[i]
                 dst_key_cache, dst_value_cache = dst[i]
                 # Copy the key blocks.
-                cache_ops.copy_cache_blocks(
+                cache_ops.swap_blocks(
                     src_key_cache, dst_key_cache, src_to_dst)
                 # Copy the value blocks.
-                cache_ops.copy_cache_blocks(
+                cache_ops.swap_blocks(
                     src_value_cache, dst_value_cache, src_to_dst)
                 event = self.events[i]
                 event.record(stream=self.cache_stream)
 
-    def copy(self, src_to_dst: Dict[int, int]) -> None:
-        self._copy_blocks(self.gpu_cache, self.gpu_cache, src_to_dst)
-
     def swap_in(self, src_to_dst: Dict[int, int]) -> None:
-        self._copy_blocks(self.cpu_cache, self.gpu_cache, src_to_dst)
+        self._swap(self.cpu_cache, self.gpu_cache, src_to_dst)
 
     def swap_out(self, src_to_dst: Dict[int, int]) -> None:
-        self._copy_blocks(self.gpu_cache, self.cpu_cache, src_to_dst)
+        self._swap(self.gpu_cache, self.cpu_cache, src_to_dst)
+
+    def _copy(
+        self,
+        src: List[KVCache],
+        dst: List[KVCache],
+        src_to_dsts: Dict[int, List[int]],
+    ) -> None:
+        with torch.cuda.stream(self.cache_stream):
+            for i in range(self.num_layers):
+                src_key_cache, src_value_cache = src[i]
+                dst_key_cache, dst_value_cache = dst[i]
+                # Copy the key blocks.
+                cache_ops.copy_blocks(
+                    src_key_cache, dst_key_cache, src_to_dsts)
+                # Copy the value blocks.
+                cache_ops.copy_blocks(
+                    src_value_cache, dst_value_cache, src_to_dsts)
+                event = self.events[i]
+                event.record(stream=self.cache_stream)
+
+    def copy(self, src_to_dsts: Dict[int, List[int]]) -> None:
+        self._copy(self.gpu_cache, self.gpu_cache, src_to_dsts)

cacheflow/worker/controller.py

@@ -1,6 +1,7 @@
 from typing import Dict, List, Union
 
 from cacheflow.master.scheduler import Scheduler
+from cacheflow.sequence import SequenceGroupInputs
 from cacheflow.worker.worker import Worker
 
 
@@ -14,7 +15,8 @@ class Controller:
         block_size: int,
         num_gpu_blocks: int,
         num_cpu_blocks: int,
-        dtype: str = 'half',
+        dtype: str,
+        seed: int,
     ) -> None:
         self.node_id = node_id
         self.num_workers = num_workers
@@ -37,6 +39,7 @@ class Controller:
                 num_gpu_blocks=num_gpu_blocks,
                 num_cpu_blocks=num_cpu_blocks,
                 dtype=dtype,
+                seed=seed,
             )
             self.workers.append(worker)
 
@@ -49,22 +52,16 @@ class Controller:
 
     def execute_stage(
         self,
-        prompt_tokens: Dict[int, List[int]],
-        generation_tokens: Dict[int, int],
-        context_lens: Dict[int, int],
-        block_tables: Dict[int, List[int]],
+        input_seq_groups: List[SequenceGroupInputs],
         blocks_to_swap_in: Dict[int, int],
         blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, int],
+        blocks_to_copy: Dict[int, List[int]],
     ) -> None:
         # FIXME: Support tensor parallelism.
         assert len(self.workers) == 1
         worker = self.workers[0]
         output = worker.execute_stage(
-            prompt_tokens,
-            generation_tokens,
-            context_lens,
-            block_tables,
+            input_seq_groups,
             blocks_to_swap_in,
             blocks_to_swap_out,
             blocks_to_copy,

cacheflow/worker/worker.py

@@ -1,9 +1,13 @@
-from typing import Dict, List, Tuple, Union
+from typing import Dict, List, Tuple
 
 import torch
 
 from cacheflow.models import get_model
+from cacheflow.models import set_seed
 from cacheflow.models import InputMetadata
+from cacheflow.sampling_params import SamplingParams
+from cacheflow.sequence import SequenceGroupInputs
+from cacheflow.sequence import SequenceOutputs
 from cacheflow.worker.cache_engine import CacheEngine
 
 
@@ -18,6 +22,7 @@ class Worker:
         num_gpu_blocks: int,
         num_cpu_blocks: int,
         dtype: str,
+        seed: int,
     ) -> None:
         self.worker_id = worker_id
         self.gpu_id = gpu_id
@@ -33,6 +38,11 @@ class Worker:
         self.head_size = self.model.config.hidden_size // self.num_heads
         self.dtype = self.model.dtype
 
+        # Set the seed.
+        # We set the seed after initializing the model to ensure that
+        # the random state is not affected by the model initialization.
+        set_seed(seed)
+
         self.cache_engine = CacheEngine(
             worker_id=worker_id,
             gpu_id=gpu_id,
@@ -49,55 +59,81 @@ class Worker:
 
     def prepare_inputs(
         self,
-        prompt_tokens: Dict[int, List[int]],    # Seq id -> List of input token ids.
-        generation_tokens: Dict[int, int],      # Seq id -> Input token id.
-        context_lens: Dict[int, int],           # Seq id -> Number of tokens participating in attention.
-        block_tables: Dict[int, List[int]],     # Seq id -> List of physical block numbers.
+        input_seq_groups: List[SequenceGroupInputs],
     ) -> Tuple[torch.LongTensor, torch.LongTensor, InputMetadata]:
-        # TODO(woosuk): Support interactive generation.
-        # Add the prompt tokens.
-        prompt_lens: List[int] = []
+        seq_groups: List[Tuple[List[int], SamplingParams]] = []
+        seq_logprobs: Dict[int, float] = {}
+        sampling_params: Dict[int, SamplingParams] = {}
         input_tokens: List[int] = []
         input_positions: List[int] = []
         slot_mapping: List[int] = []
 
-        prompt_seq_ids = sorted(prompt_tokens.keys())
-        for seq_id in prompt_seq_ids:
-            prompt_len = len(prompt_tokens[seq_id])
+        # Add prompt tokens.
+        prompt_lens: List[int] = []
+        for input_seq_group in input_seq_groups:
+            if not input_seq_group.is_prompt:
+                continue
+
+            seq_ids = list(input_seq_group.input_tokens.keys())
+            sampling_params = input_seq_group.sampling_params
+            seq_groups.append((seq_ids, sampling_params))
+            seq_logprobs.update(input_seq_group.seq_logprobs)
+
+            # Use any sequence in the group.
+            seq_id = seq_ids[0]
+
+            prompt_tokens = input_seq_group.input_tokens[seq_id]
+            prompt_len = len(prompt_tokens)
             prompt_lens.append(prompt_len)
 
-            input_tokens.extend(prompt_tokens[seq_id])
-            input_positions.extend(range(len(prompt_tokens[seq_id])))
+            input_tokens.extend(prompt_tokens)
+            # NOTE(woosuk): Here we assume that the first token in the prompt
+            # is always the first token in the sequence.
+            input_positions.extend(range(len(prompt_tokens)))
 
-            block_table = block_tables[seq_id]
+            # Compute the slot mapping.
+            block_table = input_seq_group.block_tables[seq_id]
             for i in range(prompt_len):
                 block_number = block_table[i // self.block_size]
                 block_offset = i % self.block_size
                 slot = block_number * self.block_size + block_offset
                 slot_mapping.append(slot)
 
-        # Add the generation tokens.
+        # Add generation tokens.
         max_context_len = 0
         max_num_blocks_per_seq = 0
+        context_lens: List[int] = []
         generation_block_tables: List[List[int]] = []
+        for input_seq_group in input_seq_groups:
+            if input_seq_group.is_prompt:
+                continue
 
-        generation_seq_ids = sorted(generation_tokens.keys())
-        for seq_id in generation_seq_ids:
-            input_tokens.append(generation_tokens[seq_id])
-            position_id = context_lens[seq_id] - 1
-            input_positions.append(position_id)
+            seq_ids = list(input_seq_group.input_tokens.keys())
+            sampling_params = input_seq_group.sampling_params
+            seq_groups.append((seq_ids, sampling_params))
+            seq_logprobs.update(input_seq_group.seq_logprobs)
 
-            block_table = block_tables[seq_id]
-            generation_block_tables.append(block_table)
+            for seq_id in seq_ids:
+                assert len(input_seq_group.input_tokens[seq_id]) == 1
+                generation_token = input_seq_group.input_tokens[seq_id][0]
+                input_tokens.append(generation_token)
 
-            max_context_len = max(max_context_len, context_lens[seq_id])
-            max_num_blocks_per_seq = max(
-                max_num_blocks_per_seq, len(block_table))
+                position = input_seq_group.context_len - 1
+                input_positions.append(position)
 
-            block_number = block_table[position_id // self.block_size]
-            block_offset = position_id % self.block_size
-            slot = block_number * self.block_size + block_offset
-            slot_mapping.append(slot)
+                block_table = input_seq_group.block_tables[seq_id]
+                generation_block_tables.append(block_table)
+
+                max_context_len = max(
+                    max_context_len, input_seq_group.context_len)
+                max_num_blocks_per_seq = max(
+                    max_num_blocks_per_seq, len(block_table))
+                context_lens.append(input_seq_group.context_len)
+
+                block_number = block_table[position // self.block_size]
+                block_offset = position % self.block_size
+                slot = block_number * self.block_size + block_offset
+                slot_mapping.append(slot)
 
         # Optimization: Pad the input length to be a multiple of 8.
         # This is required for utilizing the Tensor Cores in NVIDIA GPUs.
@@ -112,8 +148,7 @@ class Worker:
         slot_mapping_tensor = torch.tensor(
             slot_mapping, dtype=torch.int, device=self.device)
         context_lens_tensor = torch.tensor(
-            [context_lens[seq_id] for seq_id in generation_seq_ids],
-            dtype=torch.int, device=self.device)
+            context_lens, dtype=torch.int, device=self.device)
         padded_block_tables = [
             _pad_to_max(block_table, max_num_blocks_per_seq)
             for block_table in generation_block_tables]
@@ -121,7 +156,8 @@ class Worker:
             padded_block_tables, dtype=torch.int, device=self.device)
 
         input_metadata = InputMetadata(
-            seq_ids=prompt_seq_ids + generation_seq_ids,
+            seq_groups=seq_groups,
+            seq_logprobs=seq_logprobs,
             prompt_lens=prompt_lens,
             slot_mapping=slot_mapping_tensor,
             context_lens=context_lens_tensor,
@@ -133,14 +169,11 @@ class Worker:
     @torch.inference_mode()
     def execute_stage(
         self,
-        prompt_tokens: Dict[int, List[int]],    # Seq id -> List of input token ids.
-        generation_tokens: Dict[int, int],      # Seq id -> Input token id.
-        context_lens: Dict[int, int],           # Seq id -> Number of tokens participating in attention.
-        block_tables: Dict[int, List[int]],     # Seq id -> List of physical block numbers.
+        input_seq_groups: List[SequenceGroupInputs],
         blocks_to_swap_in: Dict[int, int],
         blocks_to_swap_out: Dict[int, int],
-        blocks_to_copy: Dict[int, int],
-    ) -> Union[torch.Tensor, Dict[int, Tuple[int, int]]]:
+        blocks_to_copy: Dict[int, List[int]],
+    ) -> Dict[int, SequenceOutputs]:
         # Issue cache operations.
         command_issued = False
         if blocks_to_swap_in:
@@ -160,7 +193,7 @@ class Worker:
 
         # Prepare input tensors.
         input_tokens, input_positions, input_metadata = self.prepare_inputs(
-            prompt_tokens, generation_tokens, context_lens, block_tables)
+            input_seq_groups)
 
         # Execute the model.
         output = self.model(

csrc/cache.cpp

@@ -1,9 +1,17 @@
 #include <torch/extension.h>
 
+#include <map>
+#include <vector>
+
+void swap_blocks(
+  torch::Tensor& src,
+  torch::Tensor& dst,
+  const std::map<int64_t, int64_t>& block_mapping);
+
 void copy_blocks(
   torch::Tensor& src,
   torch::Tensor& dst,
-  const std::map<int64_t, int64_t>& block_mapping);
+  const std::map<int64_t, std::vector<int64_t>>& block_mapping);
 
 void reshape_and_cache(
   torch::Tensor& key,
@@ -14,7 +22,11 @@ void reshape_and_cache(
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def(
-    "copy_cache_blocks",
+    "swap_blocks",
+    &swap_blocks,
+    "Swap in (out) the cache blocks from src to dst");
+  m.def(
+    "copy_blocks",
     &copy_blocks,
     "Copy the cache blocks from src to dst");
   m.def(

csrc/cache_kernels.cu

@@ -5,8 +5,9 @@
 #include <algorithm>
 #include <cassert>
 #include <map>
+#include <vector>
 
-void copy_blocks(
+void swap_blocks(
   torch::Tensor& src,
   torch::Tensor& dst,
   const std::map<int64_t, int64_t>& block_mapping) {
@@ -43,6 +44,35 @@ void copy_blocks(
   }
 }
 
+void copy_blocks(
+  torch::Tensor& src,
+  torch::Tensor& dst,
+  const std::map<int64_t, std::vector<int64_t>>& block_mapping) {
+  torch::Device src_device = src.device();
+  torch::Device dst_device = dst.device();
+  assert(src_device.is_cuda() && dst_device.is_cuda());
+  cudaMemcpyKind memcpy_type = cudaMemcpyDeviceToDevice;
+
+  void *src_ptr = src.data_ptr();
+  void *dst_ptr = dst.data_ptr();
+
+  const int64_t block_size_in_bytes = src.element_size() * src[0].numel();
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  for (const auto& pair : block_mapping) {
+    int64_t src_block_number = pair.first;
+    for (int64_t dst_block_number : pair.second) {
+      int64_t src_offset = src_block_number * block_size_in_bytes;
+      int64_t dst_offset = dst_block_number * block_size_in_bytes;
+      cudaMemcpyAsync(
+        dst_ptr + dst_offset,
+        src_ptr + src_offset,
+        block_size_in_bytes,
+        memcpy_type,
+        stream);
+    }
+  }
+}
+
 template<typename scalar_t>
 __global__ void reshape_and_cache_kernel(
   const scalar_t* __restrict__ key,     // [num_tokens, num_heads, head_size]

server.py

@@ -15,6 +15,8 @@ parser.add_argument('--num-gpu-blocks', type=int, default=1024, help='number of
 parser.add_argument('--num-cpu-blocks', type=int, default=32, help='number of CPU blocks (per GPU)')
 # NOTE(woosuk): If FlashAttention is used, the float data type is not supported.
 parser.add_argument('--dtype', type=str, default='half', choices=['half', 'float'], help='data type')
+# TODO(woosuk): Support fine-grained seeds (e.g., seed per request).
+parser.add_argument('--seed', type=int, default=0, help='random seed')
 args = parser.parse_args()
 
 
@@ -30,6 +32,7 @@ def main():
             num_gpu_blocks=args.num_gpu_blocks,
             num_cpu_blocks=args.num_cpu_blocks,
             dtype=args.dtype,
+            seed=args.seed,
         )
         controllers.append(controller)
 
@@ -52,18 +55,18 @@ def main():
         controllers[i].set_next(controllers[i + 1])
     controllers[-1].set_next(scheduler)
 
+    # Test the following inputs.
     test_inputs = [
-        'Ion Stoica is a',
-        'UC Berkeley is',
-        'The future of cloud computing is',
+        ('Ion Stoica is a', {'n': 4, 'use_beam_search': True, 'temperature': 0.0}),
+        ('UC Berkeley is', {'n': 3, 'temperature': 0.8, 'top_p': 0.99}),
+        ('The future of cloud computing is', {}),   # Use default parameters.
     ]
-
-    # FIXME
     while True:
         if test_inputs:
-            frontend.query(test_inputs.pop())
+            text, sampling_params = test_inputs.pop(0)
+            frontend.query(text, **sampling_params)
         scheduler.step()
-        if not scheduler.pending and not scheduler.running:
+        if not (scheduler.pending or scheduler.running or test_inputs):
             break