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fix all iterrows callsites

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This commit is contained in:
Xi Yan 2025-03-15 17:14:10 -07:00
parent f117407af6
commit b561cfd902
6 changed files with 56 additions and 163 deletions
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4
llama_stack/distribution/ui/page/evaluations/native_eval.py
View file

@ -229,9 +229,7 @@ def run_evaluation_3():
output_res[scoring_fn] = []
output_res[scoring_fn].append(eval_res.scores[scoring_fn].score_rows[0])
progress_text_container.write(
f"Expand to see current processed result ({i + 1} / {len(rows)})"
)
progress_text_container.write(f"Expand to see current processed result ({i + 1} / {len(rows)})")
results_container.json(eval_res, expanded=2)
progress_bar.progress(1.0, text="Evaluation complete!")

72
llama_stack/providers/inline/eval/meta_reference/eval.py
View file

@ -89,16 +89,10 @@ class MetaReferenceEvalImpl(
dataset_id = task_def.dataset_id
scoring_functions = task_def.scoring_functions
dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
validate_dataset_schema(
dataset_def.dataset_schema, get_valid_schemas(Api.eval.value)
)
validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.eval.value))
all_rows = await self.datasetio_api.iterrows(
dataset_id=dataset_id,
limit=(
-1
if benchmark_config.num_examples is None
else benchmark_config.num_examples
),
limit=(-1 if benchmark_config.num_examples is None else benchmark_config.num_examples),
)
res = await self.evaluate_rows(
benchmark_id=benchmark_id,
@ -124,14 +118,10 @@ class MetaReferenceEvalImpl(
for i, x in tqdm(enumerate(input_rows)):
assert ColumnName.chat_completion_input.value in x, "Invalid input row"
input_messages = json.loads(x[ColumnName.chat_completion_input.value])
input_messages = [
UserMessage(**x) for x in input_messages if x["role"] == "user"
]
input_messages = [UserMessage(**x) for x in input_messages if x["role"] == "user"]
# NOTE: only single-turn agent generation is supported. Create a new session for each input row
session_create_response = await self.agents_api.create_agent_session(
agent_id, f"session-{i}"
)
session_create_response = await self.agents_api.create_agent_session(agent_id, f"session-{i}")
session_id = session_create_response.session_id
turn_request = dict(
@ -140,12 +130,7 @@ class MetaReferenceEvalImpl(
messages=input_messages,
stream=True,
)
turn_response = [
chunk
async for chunk in await self.agents_api.create_agent_turn(
**turn_request
)
]
turn_response = [chunk async for chunk in await self.agents_api.create_agent_turn(**turn_request)]
final_event = turn_response[-1].event.payload
# check if there's a memory retrieval step and extract the context
@ -154,14 +139,10 @@ class MetaReferenceEvalImpl(
if step.step_type == StepType.tool_execution.value:
for tool_response in step.tool_responses:
if tool_response.tool_name == MEMORY_QUERY_TOOL:
memory_rag_context = " ".join(
x.text for x in tool_response.content
)
memory_rag_context = " ".join(x.text for x in tool_response.content)
agent_generation = {}
agent_generation[ColumnName.generated_answer.value] = (
final_event.turn.output_message.content
)
agent_generation[ColumnName.generated_answer.value] = final_event.turn.output_message.content
if memory_rag_context:
agent_generation[ColumnName.context.value] = memory_rag_context
@ -173,9 +154,7 @@ class MetaReferenceEvalImpl(
self, input_rows: List[Dict[str, Any]], benchmark_config: BenchmarkConfig
) -> List[Dict[str, Any]]:
candidate = benchmark_config.eval_candidate
assert (
candidate.sampling_params.max_tokens is not None
), "SamplingParams.max_tokens must be provided"
assert candidate.sampling_params.max_tokens is not None, "SamplingParams.max_tokens must be provided"
generations = []
for x in tqdm(input_rows):
@ -186,39 +165,21 @@ class MetaReferenceEvalImpl(
content=input_content,
sampling_params=candidate.sampling_params,
)
generations.append(
{
ColumnName.generated_answer.value: response.completion_message.content
}
)
generations.append({ColumnName.generated_answer.value: response.completion_message.content})
elif ColumnName.chat_completion_input.value in x:
chat_completion_input_json = json.loads(
x[ColumnName.chat_completion_input.value]
)
input_messages = [
UserMessage(**x)
for x in chat_completion_input_json
if x["role"] == "user"
]
chat_completion_input_json = json.loads(x[ColumnName.chat_completion_input.value])
input_messages = [UserMessage(**x) for x in chat_completion_input_json if x["role"] == "user"]
messages = []
if candidate.system_message:
messages.append(candidate.system_message)
messages += [
SystemMessage(**x)
for x in chat_completion_input_json
if x["role"] == "system"
]
messages += [SystemMessage(**x) for x in chat_completion_input_json if x["role"] == "system"]
messages += input_messages
response = await self.inference_api.chat_completion(
model_id=candidate.model,
messages=messages,
sampling_params=candidate.sampling_params,
)
generations.append(
{
ColumnName.generated_answer.value: response.completion_message.content
}
)
generations.append({ColumnName.generated_answer.value: response.completion_message.content})
else:
raise ValueError("Invalid input row")
@ -241,8 +202,7 @@ class MetaReferenceEvalImpl(
# scoring with generated_answer
score_input_rows = [
input_r | generated_r
for input_r, generated_r in zip(input_rows, generations, strict=False)
input_r | generated_r for input_r, generated_r in zip(input_rows, generations, strict=False)
]
if benchmark_config.scoring_params is not None:
@ -251,9 +211,7 @@ class MetaReferenceEvalImpl(
for scoring_fn_id in scoring_functions
}
else:
scoring_functions_dict = {
scoring_fn_id: None for scoring_fn_id in scoring_functions
}
scoring_functions_dict = {scoring_fn_id: None for scoring_fn_id in scoring_functions}
score_response = await self.scoring_api.score(
input_rows=score_input_rows, scoring_functions=scoring_functions_dict

72
llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py
View file

@ -17,7 +17,8 @@ import torch
from torch import nn
from torch.optim import Optimizer
from torch.utils.data import DataLoader, DistributedSampler
from torchtune import modules, training, utils as torchtune_utils
from torchtune import modules, training
from torchtune import utils as torchtune_utils
from torchtune.data import padded_collate_sft
from torchtune.modules.loss import CEWithChunkedOutputLoss
from torchtune.modules.peft import (
@ -88,9 +89,7 @@ class LoraFinetuningSingleDevice:
self.job_uuid = job_uuid
self.training_config = training_config
if not isinstance(algorithm_config, LoraFinetuningConfig):
raise ValueError(
"You need to speicifc LoraFinetuningConfig for LoRA finetuning"
)
raise ValueError("You need to speicifc LoraFinetuningConfig for LoRA finetuning")
self.algorithm_config = algorithm_config
self._device = torchtune_utils.get_device()
self._dtype = training.get_dtype(training_config.dtype, device=self._device)
@ -99,10 +98,7 @@ class LoraFinetuningSingleDevice:
def model_checkpoint_dir(model) -> str:
checkpoint_dir = Path(model_local_dir(model.descriptor()))
paths = [
Path(checkpoint_dir / f"consolidated.{ext}")
for ext in ["pth", "00.pth"]
]
paths = [Path(checkpoint_dir / f"consolidated.{ext}") for ext in ["pth", "00.pth"]]
if not any(p.exists() for p in paths):
checkpoint_dir = checkpoint_dir / "original"
@ -117,9 +113,7 @@ class LoraFinetuningSingleDevice:
else:
model = resolve_model(self.model_id)
if model is None:
raise ValueError(
f"{self.model_id} not found. Your model id should be in the llama models SKU list"
)
raise ValueError(f"{self.model_id} not found. Your model id should be in the llama models SKU list")
self.checkpoint_dir = model_checkpoint_dir(model)
self._output_dir = str(DEFAULT_CHECKPOINT_DIR)
@ -191,9 +185,7 @@ class LoraFinetuningSingleDevice:
self._tokenizer = await self._setup_tokenizer()
log.info("Tokenizer is initialized.")
self._optimizer = await self._setup_optimizer(
optimizer_config=self.training_config.optimizer_config
)
self._optimizer = await self._setup_optimizer(optimizer_config=self.training_config.optimizer_config)
log.info("Optimizer is initialized.")
self._loss_fn = CEWithChunkedOutputLoss()
@ -221,13 +213,8 @@ class LoraFinetuningSingleDevice:
# by the dataloader and the max_steps_per_epoch param set by the user and is used
# for logging and tracking training state. This should be computed after the dataloader
# has been setup
self._steps_per_epoch = (
len(self._training_dataloader) // self._gradient_accumulation_steps
)
if (
self.max_steps_per_epoch is not None
and self.max_steps_per_epoch < self._steps_per_epoch
):
self._steps_per_epoch = len(self._training_dataloader) // self._gradient_accumulation_steps
if self.max_steps_per_epoch is not None and self.max_steps_per_epoch < self._steps_per_epoch:
self._steps_per_epoch = self.max_steps_per_epoch
self.global_step = self.epochs_run * self._steps_per_epoch
@ -241,9 +228,7 @@ class LoraFinetuningSingleDevice:
log.info("Learning rate scheduler is initialized.")
# Used to ignore labels for loss computation
self.ignore_labels_cache = torch.full(
(self._batch_size, 1), self._loss_fn.ignore_index, device=self._device
)
self.ignore_labels_cache = torch.full((self._batch_size, 1), self._loss_fn.ignore_index, device=self._device)
def _log_memory_stats(self):
# torchtune raises: "Logging memory stats is not supported on CPU devices"; do nothing
@ -284,13 +269,9 @@ class LoraFinetuningSingleDevice:
set_trainable_params(model, self.adapter_params)
if enable_activation_checkpointing:
training.set_activation_checkpointing(
model, auto_wrap_policy={modules.TransformerSelfAttentionLayer}
)
training.set_activation_checkpointing(model, auto_wrap_policy={modules.TransformerSelfAttentionLayer})
base_missing, base_unexpected = model.load_state_dict(
base_model_state_dict, strict=False
)
base_missing, base_unexpected = model.load_state_dict(base_model_state_dict, strict=False)
# This is for any adapters that need to be initialized after base weights
# have been loaded (e.g. DoRA).
@ -299,9 +280,7 @@ class LoraFinetuningSingleDevice:
if hasattr(m, "initialize_dora_magnitude"):
m.initialize_dora_magnitude()
if lora_weights_state_dict:
lora_missing, lora_unexpected = model.load_state_dict(
lora_weights_state_dict, strict=False
)
lora_missing, lora_unexpected = model.load_state_dict(lora_weights_state_dict, strict=False)
else:
lora_missing, lora_unexpected = None, None
validate_missing_and_unexpected_for_lora(
@ -315,14 +294,10 @@ class LoraFinetuningSingleDevice:
)
# Validate model adapter params were loaded in with the expected dtype
training.validate_expected_param_dtype(
self.adapter_params.items(), dtype=self._dtype
)
training.validate_expected_param_dtype(self.adapter_params.items(), dtype=self._dtype)
# activation offloading
self.activations_handling_ctx = training.get_act_offloading_ctx_manager(
model, enable_activation_offloading
)
self.activations_handling_ctx = training.get_act_offloading_ctx_manager(model, enable_activation_offloading)
self._log_memory_stats()
@ -458,9 +433,7 @@ class LoraFinetuningSingleDevice:
# Shift labels to compute loss
# equivalent to doing labels[..., 1:] and logits[..., :-1, :]
# But this way we dont need to slice the logits. We just add an ignore index to labels.
labels = torch.hstack(
(labels[..., 1:], self.ignore_labels_cache[: labels.shape[0]])
)
labels = torch.hstack((labels[..., 1:], self.ignore_labels_cache[: labels.shape[0]]))
if not isinstance(logits, list):
labels = labels.reshape(-1)
logits = logits.reshape(-1, logits.size(-1))
@ -489,9 +462,7 @@ class LoraFinetuningSingleDevice:
for curr_epoch in range(self.epochs_run, self.total_epochs):
# Update the sampler to ensure data is correctly shuffled across epochs
# in case shuffle is True
metric_logger = DiskLogger(
log_dir=self._output_dir + f"/{self.model_id}-sft-{curr_epoch}/log"
)
metric_logger = DiskLogger(log_dir=self._output_dir + f"/{self.model_id}-sft-{curr_epoch}/log")
self._training_sampler.set_epoch(curr_epoch)
loss_to_log = 0.0
@ -499,8 +470,7 @@ class LoraFinetuningSingleDevice:
for idx, batch in enumerate(self._training_dataloader):
if (
self.max_steps_per_epoch is not None
and (idx // self._gradient_accumulation_steps)
== self.max_steps_per_epoch
and (idx // self._gradient_accumulation_steps) == self.max_steps_per_epoch
):
break
@ -508,9 +478,7 @@ class LoraFinetuningSingleDevice:
# Calculate the number of unmasked tokens in the current batch
# and increment the total number of tokens seen in the step
current_num_tokens = (
batch["labels"] != self._loss_fn.ignore_index
).sum()
current_num_tokens = (batch["labels"] != self._loss_fn.ignore_index).sum()
num_tokens += current_num_tokens
# Loss is normalized by default so we multiply by the number of tokens
@ -535,9 +503,7 @@ class LoraFinetuningSingleDevice:
loss_to_log = running_loss.item() / num_tokens
pbar.update(1)
pbar.set_description(
f"{curr_epoch + 1}|{self.global_step}|Loss: {loss_to_log}"
)
pbar.set_description(f"{curr_epoch + 1}|{self.global_step}|Loss: {loss_to_log}")
time_per_step = time.perf_counter() - t0
log_dict = {

20
llama_stack/providers/inline/scoring/basic/scoring.py
View file

@ -64,15 +64,11 @@ class BasicScoringImpl(
async def list_scoring_functions(self) -> List[ScoringFn]:
scoring_fn_defs_list = [
fn_def
for impl in self.scoring_fn_id_impls.values()
for fn_def in impl.get_supported_scoring_fn_defs()
fn_def for impl in self.scoring_fn_id_impls.values() for fn_def in impl.get_supported_scoring_fn_defs()
]
for f in scoring_fn_defs_list:
assert f.identifier.startswith(
"basic"
), "All basic scoring fn must have identifier prefixed with 'basic'! "
assert f.identifier.startswith("basic"), "All basic scoring fn must have identifier prefixed with 'basic'! "
return scoring_fn_defs_list
@ -86,9 +82,7 @@ class BasicScoringImpl(
save_results_dataset: bool = False,
) -> ScoreBatchResponse:
dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
validate_dataset_schema(
dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value)
)
validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value))
all_rows = await self.datasetio_api.iterrows(
dataset_id=dataset_id,
@ -118,12 +112,8 @@ class BasicScoringImpl(
raise ValueError(f"Scoring function {scoring_fn_id} is not supported.")
scoring_fn = self.scoring_fn_id_impls[scoring_fn_id]
scoring_fn_params = scoring_functions.get(scoring_fn_id, None)
score_results = await scoring_fn.score(
input_rows, scoring_fn_id, scoring_fn_params
)
agg_results = await scoring_fn.aggregate(
score_results, scoring_fn_id, scoring_fn_params
)
score_results = await scoring_fn.score(input_rows, scoring_fn_id, scoring_fn_params)
agg_results = await scoring_fn.aggregate(score_results, scoring_fn_id, scoring_fn_params)
res[scoring_fn_id] = ScoringResult(
score_rows=score_results,
aggregated_results=agg_results,

33
llama_stack/providers/inline/scoring/braintrust/braintrust.py
View file

@ -122,12 +122,10 @@ class BraintrustScoringImpl(
self.datasets_api = datasets_api
self.braintrust_evaluators = {
entry.identifier: entry.evaluator
for entry in SUPPORTED_BRAINTRUST_SCORING_FN_ENTRY
entry.identifier: entry.evaluator for entry in SUPPORTED_BRAINTRUST_SCORING_FN_ENTRY
}
self.supported_fn_defs_registry = {
entry.identifier: entry.fn_def
for entry in SUPPORTED_BRAINTRUST_SCORING_FN_ENTRY
entry.identifier: entry.fn_def for entry in SUPPORTED_BRAINTRUST_SCORING_FN_ENTRY
}
async def initialize(self) -> None: ...
@ -137,16 +135,14 @@ class BraintrustScoringImpl(
async def list_scoring_functions(self) -> List[ScoringFn]:
scoring_fn_defs_list = list(self.supported_fn_defs_registry.values())
for f in scoring_fn_defs_list:
assert f.identifier.startswith(
"braintrust"
), "All braintrust scoring fn must have identifier prefixed with 'braintrust'! "
assert f.identifier.startswith("braintrust"), (
"All braintrust scoring fn must have identifier prefixed with 'braintrust'! "
)
return scoring_fn_defs_list
async def register_scoring_function(self, scoring_fn: ScoringFn) -> None:
raise NotImplementedError(
"Registering scoring function not allowed for braintrust provider"
)
raise NotImplementedError("Registering scoring function not allowed for braintrust provider")
async def set_api_key(self) -> None:
# api key is in the request headers
@ -169,17 +165,13 @@ class BraintrustScoringImpl(
await self.set_api_key()
dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
validate_dataset_schema(
dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value)
)
validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value))
all_rows = await self.datasetio_api.iterrows(
dataset_id=dataset_id,
limit=-1,
)
res = await self.score(
input_rows=all_rows.data, scoring_functions=scoring_functions
)
res = await self.score(input_rows=all_rows.data, scoring_functions=scoring_functions)
if save_results_dataset:
# TODO: persist and register dataset on to server for reading
# self.datasets_api.register_dataset()
@ -220,13 +212,8 @@ class BraintrustScoringImpl(
if scoring_fn_id not in self.supported_fn_defs_registry:
raise ValueError(f"Scoring function {scoring_fn_id} is not supported.")
score_results = [
await self.score_row(input_row, scoring_fn_id)
for input_row in input_rows
]
aggregation_functions = self.supported_fn_defs_registry[
scoring_fn_id
].params.aggregation_functions
score_results = [await self.score_row(input_row, scoring_fn_id) for input_row in input_rows]
aggregation_functions = self.supported_fn_defs_registry[scoring_fn_id].params.aggregation_functions
# override scoring_fn params if provided
if scoring_functions[scoring_fn_id] is not None:

18
llama_stack/providers/inline/scoring/llm_as_judge/scoring.py
View file

@ -54,9 +54,9 @@ class LlmAsJudgeScoringImpl(
scoring_fn_defs_list = self.llm_as_judge_fn.get_supported_scoring_fn_defs()
for f in self.llm_as_judge_fn.get_supported_scoring_fn_defs():
assert f.identifier.startswith(
"llm-as-judge"
), "All llm-as-judge scoring fn must have identifier prefixed with 'llm-as-judge'! "
assert f.identifier.startswith("llm-as-judge"), (
"All llm-as-judge scoring fn must have identifier prefixed with 'llm-as-judge'! "
)
return scoring_fn_defs_list
@ -70,9 +70,7 @@ class LlmAsJudgeScoringImpl(
save_results_dataset: bool = False,
) -> ScoreBatchResponse:
dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
validate_dataset_schema(
dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value)
)
validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value))
all_rows = await self.datasetio_api.iterrows(
dataset_id=dataset_id,
@ -100,12 +98,8 @@ class LlmAsJudgeScoringImpl(
for scoring_fn_id in scoring_functions.keys():
scoring_fn = self.llm_as_judge_fn
scoring_fn_params = scoring_functions.get(scoring_fn_id, None)
score_results = await scoring_fn.score(
input_rows, scoring_fn_id, scoring_fn_params
)
agg_results = await scoring_fn.aggregate(
score_results, scoring_fn_id, scoring_fn_params
)
score_results = await scoring_fn.score(input_rows, scoring_fn_id, scoring_fn_params)
agg_results = await scoring_fn.aggregate(score_results, scoring_fn_id, scoring_fn_params)
res[scoring_fn_id] = ScoringResult(
score_rows=score_results,
aggregated_results=agg_results,