Fixing some legacy behavior (big swapout of serverless on legacy stuff). (#1937)
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@ -21,10 +21,28 @@ use tracing_subscriber::EnvFilter;
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mod env_runtime;
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#[derive(Deserialize)]
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struct RawConfig {
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max_position_embeddings: Option<usize>,
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n_positions: Option<usize>,
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max_seq_len: Option<usize>,
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}
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#[derive(Deserialize)]
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struct Config {
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max_position_embeddings: Option<usize>,
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max_seq_len: Option<usize>,
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}
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impl From<RawConfig> for Config {
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fn from(other: RawConfig) -> Self {
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let max_position_embeddings = other
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.max_position_embeddings
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.or(other.max_seq_len)
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.or(other.n_positions);
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Config {
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max_position_embeddings,
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}
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}
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}
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#[derive(Clone, Copy, Debug, ValueEnum)]
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@ -1309,13 +1327,13 @@ fn main() -> Result<(), LauncherError> {
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};
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let content = std::fs::read_to_string(filename)?;
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let config: Config = serde_json::from_str(&content)?;
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let config: RawConfig = serde_json::from_str(&content)?;
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let config: Config = config.into();
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// Quantization usually means you're even more RAM constrained.
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let max_default = 4096;
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let max_position_embeddings = match (config.max_position_embeddings, config.max_seq_len) {
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(Some(max_position_embeddings), _) | (None, Some(max_position_embeddings)) => {
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if let Some(max_position_embeddings) = config.max_position_embeddings {
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if max_position_embeddings > max_default {
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let max = max_position_embeddings;
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if args.max_input_tokens.is_none()
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@ -1324,18 +1342,15 @@ fn main() -> Result<(), LauncherError> {
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{
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tracing::info!("Model supports up to {max} but tgi will now set its default to {max_default} instead. This is to save VRAM by refusing large prompts in order to allow more users on the same hardware. You can increase that size using `--max-batch-prefill-tokens={} --max-total-tokens={max} --max-input-tokens={}`.", max + 50, max - 1);
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}
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max_default
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Ok(max_default)
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} else {
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max_position_embeddings
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}
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}
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_ => {
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return Err(Box::new(LauncherError::ArgumentValidation(
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"no max defined".to_string(),
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)));
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}
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};
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Ok(max_position_embeddings)
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}
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} else {
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Err(Box::new(LauncherError::ArgumentValidation(
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"no max defined".to_string(),
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)))
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}
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};
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let max_position_embeddings: usize = get_max_position_embeddings().unwrap_or(4096);
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@ -472,6 +472,7 @@ def get_model(
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)
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elif model_type == GPT2:
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if FLASH_ATTENTION:
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try:
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return FlashGPT2(
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model_id,
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revision,
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@ -480,6 +481,17 @@ def get_model(
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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except RuntimeError as e:
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# Lots of legacy models with various weight names.
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logger.warning(f"Couldn't load flash gpt2 variant: {e}")
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return CausalLM(
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model_id,
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revision,
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quantize=quantize,
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speculator=speculator,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif sharded:
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raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded GPT-2"))
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else:
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@ -14,13 +14,21 @@ from typing import List, Optional
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from text_generation_server.cache import Cache
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from text_generation_server.interceptor import ExceptionInterceptor
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from text_generation_server.models import Model, get_model
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from text_generation_server.models.pali_gemma import PaliGemmaBatch
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from text_generation_server.models.vlm_causal_lm import (
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try:
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from text_generation_server.models.pali_gemma import PaliGemmaBatch
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from text_generation_server.models.vlm_causal_lm import (
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VlmCausalLMBatch,
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)
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)
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from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
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VLM_BATCH_TYPES = {PaliGemmaBatch, VlmCausalLMBatch, IdeficsCausalLMBatch}
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except (ImportError, NotImplementedError):
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# These imports can fail on CPU/Non flash.
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VLM_BATCH_TYPES = set()
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from text_generation_server.pb import generate_pb2_grpc, generate_pb2
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from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
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from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
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from text_generation_server.models.globals import set_model_id
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@ -96,11 +104,9 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
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except ImportError:
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pass
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if self.model.batch_type in {
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IdeficsCausalLMBatch,
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VlmCausalLMBatch,
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PaliGemmaBatch,
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}: # Hack, i would rather use kwargs in the `from_pb` call
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if (
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self.model.batch_type in VLM_BATCH_TYPES
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): # Hack, i would rather use kwargs in the `from_pb` call
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batch = self.model.batch_type.from_pb_processor(
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request.batch,
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self.model.tokenizer,
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@ -121,11 +127,9 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
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async def Prefill(self, request, context):
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start = time.time_ns()
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if self.model.batch_type in {
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IdeficsCausalLMBatch,
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VlmCausalLMBatch,
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PaliGemmaBatch,
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}: # Hack, i would rather use kwargs in the `from_pb` call
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if (
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self.model.batch_type in VLM_BATCH_TYPES
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): # Hack, i would rather use kwargs in the `from_pb` call
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batch = self.model.batch_type.from_pb_processor(
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request.batch,
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self.model.tokenizer,
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