working flash + paged through transformers
This commit is contained in:
Felix Marty
parent
be2d38032a
commit
cb37c551ab
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@ -12,6 +12,7 @@ from pathlib import Path
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from text_generation_server.utils.speculate import get_speculate, set_speculate
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from text_generation_server.models.model import Model
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from text_generation_server.models.causal_lm import CausalLM
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from text_generation_server.models.causal_lm_ragged import CausalLMRagged
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from text_generation_server.models.flash_causal_lm import FlashCausalLM
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from text_generation_server.models.bloom import BLOOMSharded
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from text_generation_server.models.mpt import MPTSharded
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@ -588,7 +589,7 @@ def get_model(
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)
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elif model_type == LLAMA or model_type == BAICHUAN or model_type == PHI3:
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if FLASH_ATTENTION:
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if FLASH_ATTENTION and False:
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return FlashLlama(
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model_id,
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revision,
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@ -601,7 +602,8 @@ def get_model(
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elif sharded:
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raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Llama"))
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else:
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return CausalLM(
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logger.info("LOADING CAUSALLM!!!!!!!!!!!!!!!!!!")
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return CausalLMRagged(
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model_id,
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revision,
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quantize=quantize,
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@ -0,0 +1,630 @@
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import torch
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import time
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from dataclasses import dataclass
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from opentelemetry import trace
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from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
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from typing import Optional, Tuple, List, Type, Dict
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from text_generation_server.utils.import_utils import SYSTEM
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from text_generation_server.models import Model
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from text_generation_server.utils.chunks import concat_text_chunks
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from text_generation_server.utils.tokens import batch_top_tokens
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from text_generation_server.models.types import (
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Batch,
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Tokens,
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Generation,
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GeneratedText,
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)
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from text_generation_server.pb import generate_pb2
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from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
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from text_generation_server.models.flash_causal_lm import FlashCausalLMBatch
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from text_generation_server.utils.import_utils import (
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empty_cache,
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synchronize,
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get_free_memory,
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)
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from text_generation_server.utils.speculate import get_speculate
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from text_generation_server.utils.dist import MEMORY_FRACTION
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tracer = trace.get_tracer(__name__)
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from transformers.cache_utils import PagedCache
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from loguru import logger
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# Why define it here?
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BLOCK_SIZE: int = 16
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class CausalLMRagged(Model):
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def __init__(
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self,
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model_id: str,
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revision: Optional[str] = None,
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quantize: Optional[str] = None,
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speculator: Optional[str] = None,
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dtype: Optional[torch.dtype] = None,
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trust_remote_code: bool = False,
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):
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if speculator:
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raise RuntimeError("Speculator decoding is not enabled for AutoModel")
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if torch.cuda.is_available():
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device = torch.device("cuda:0") # TODO felix: fix support for accelerate
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dtype = torch.float16 if dtype is None else dtype
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else:
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if quantize:
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raise ValueError("quantization is not available on CPU")
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device = torch.device("cpu")
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dtype = torch.float32 if dtype is None else dtype
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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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revision=revision,
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padding_side="left",
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truncation_side="left",
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trust_remote_code=trust_remote_code,
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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revision=revision,
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torch_dtype=dtype,
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device_map=None,
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load_in_8bit=quantize == "bitsandbytes",
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trust_remote_code=trust_remote_code,
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attn_implementation="flash_attention_2",
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)
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if (
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torch.cuda.is_available()
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and torch.cuda.device_count() == 1
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and quantize != "bitsandbytes"
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):
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model = model.cuda()
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self.kv_cache = []
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self.num_layers = len(model.model.layers)
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self.num_kv_heads = model.config.num_key_value_heads
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self.head_size = model.config.hidden_size // model.config.num_attention_heads
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if tokenizer.pad_token_id is None:
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if model.config.pad_token_id is not None:
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tokenizer.pad_token_id = model.config.pad_token_id
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elif model.config.eos_token_id is not None:
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tokenizer.pad_token_id = model.config.eos_token_id
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elif tokenizer.eos_token_id is not None:
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tokenizer.pad_token_id = tokenizer.eos_token_id
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else:
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tokenizer.add_special_tokens({"pad_token": "[PAD]"})
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super().__init__(
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model_id=model_id,
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model=model,
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tokenizer=tokenizer,
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requires_padding=False,
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dtype=dtype,
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device=device,
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)
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def warmup(self, batch: FlashCausalLMBatch):
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# The warmup batch is the biggest batch we could ever receive
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empty_cache()
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try:
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self.init_kv_cache(
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batch.num_blocks,
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self.num_layers,
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self.num_kv_heads,
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self.head_size,
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self.dtype,
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self.device,
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)
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max_bt = batch.max_blocks
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max_s = max_bt * BLOCK_SIZE
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_, batch, _ = self.generate_token(batch)
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except torch.cuda.OutOfMemoryError as e:
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raise RuntimeError(
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f"Not enough memory to handle {len(batch.input_ids)} prefill tokens. "
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f"You need to decrease `--max-batch-prefill-tokens`"
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) from e
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synchronize(self.device)
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# Inspired by the original implementation in [vllm](https://github.com/vllm-project/vllm)
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# Calculate the number of blocks that can be allocated with the free memory
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dtype_size = torch.tensor([], dtype=self.dtype).element_size()
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cache_block_size = BLOCK_SIZE * self.num_kv_heads * self.head_size
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total_cache_size = self.num_layers * cache_block_size * 2 * dtype_size
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free_memory = get_free_memory(self.device, MEMORY_FRACTION)
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batch_num_blocks = batch.num_blocks if batch is not None else 0
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num_blocks = (
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# Leave 5% for some wiggle room
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int((free_memory * 0.95) // total_cache_size)
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# Add batch.num_blocks as we allocated it above, so it is included in the peak memory.
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+ batch_num_blocks
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)
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del batch
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self.init_kv_cache(
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num_blocks,
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self.num_layers,
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self.num_kv_heads,
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self.head_size,
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self.dtype,
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self.device,
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)
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return int(num_blocks * BLOCK_SIZE)
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def init_kv_cache(
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self,
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num_blocks: int,
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num_layers: int,
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num_heads: int,
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head_size: int,
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dtype: torch.dtype,
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device: torch.device,
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):
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self.kv_cache = []
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empty_cache()
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element_size = torch.tensor([], dtype=dtype).element_size()
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if SYSTEM == "ipex" and device.type == "xpu":
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raise ValueError("Untested. Please open an issue")
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else:
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x = BLOCK_SIZE // element_size
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if SYSTEM == "ipex" and device == torch.device("cpu"):
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raise ValueError("Untested. Please open an issue")
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self.kv_cache = [
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(
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torch.empty(
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(num_blocks, num_heads, head_size // x, BLOCK_SIZE, x),
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dtype=dtype,
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device=device,
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),
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torch.empty(
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(num_blocks, num_heads, head_size, BLOCK_SIZE),
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dtype=dtype,
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device=device,
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),
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)
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for _ in range(num_layers)
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]
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@property
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def batch_type(self) -> Type[FlashCausalLMBatch]:
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return FlashCausalLMBatch
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def decode(self, generated_ids: List[int]) -> str:
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return self.tokenizer.decode(
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generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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def forward(
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self, batch: FlashCausalLMBatch
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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# NOTE: adapter_data: not supported
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input_ids = batch.input_ids
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position_ids = batch.position_ids
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cu_seqlen_prefill = batch.cu_seqlen_prefill
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kv_cache = self.kv_cache
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block_tables = batch.block_tables_tensor
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slots = batch.slots[batch.slot_indices]
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input_lengths = batch.input_lengths_tensor
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max_s = batch.max_seqlen
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lm_head_indices = batch.prefill_head_indices
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# TODO felix: support window attention
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# if cu_seqlen_prefill is None and self.max_past() is not None:
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# # In decode, not prefill, we're actually overwriting the KV-cache
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# # in a circular buffer mode.
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# # This makes sure the max_s for the decode pass is correct.
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# max_s = min(self.max_past(), max_s)
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bs = input_ids.shape[0]
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logits = self.model.forward(
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input_ids=input_ids,
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position_ids=position_ids,
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past_key_values=PagedCache(),
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cu_seqlen_prefill=cu_seqlen_prefill,
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kv_cache=kv_cache,
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block_tables=block_tables,
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slots=slots,
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input_lengths=input_lengths,
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max_s=max_s,
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prefill_cache_indices=batch.prefill_cache_indices,
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lm_head_indices=lm_head_indices,
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cache_position=False,
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return_dict=False,
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)[0]
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if lm_head_indices is not None:
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logits = logits[lm_head_indices]
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if batch.prefill_cache_indices is not None:
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batch.prefill_cache_indices = None
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speculative_logits = None
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return logits, speculative_logits
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@tracer.start_as_current_span("generate_token")
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def generate_token(
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self, batch: FlashCausalLMBatch
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) -> Tuple[List[Generation], Optional[FlashCausalLMBatch], Tuple[int, int]]:
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start = time.time_ns()
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prefill = batch.cu_seqlen_prefill is not None
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prefill_logprobs = batch.prefill_next_token_indices is not None
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# Update adapter indices for speculative tokens (if present)
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# adapter_meta = batch.adapter_meta
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# if batch.speculative_ids is not None:
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# B, speculative_length = batch.speculative_ids.shape
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# new_length = speculative_length + 1
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# adapter_indices = (
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# adapter_meta.adapter_indices.unsqueeze(-1)
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# .expand(B, new_length)
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# .reshape(-1)
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# )
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# adapter_segments = adapter_meta.adapter_segments * new_length
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# adapter_meta = AdapterBatchMetadata(
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# adapter_indices=adapter_indices,
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# adapter_set=adapter_meta.adapter_set,
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# adapter_segments=adapter_segments,
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# segment_indices=adapter_meta.segment_indices,
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# )
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# Assign pointers to adapter weights
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# TODO(travis): don't update this if indices haven't changed
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# adapter_data = AdapterBatchData.from_meta(
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# adapter_meta,
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# self.layer_to_adapter_weights,
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# prefill,
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# batch.prefill_head_indices,
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# )
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logger.info(f"batch.input_ids {batch.input_ids}")
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out, speculative_logits = self.forward(batch)
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logger.info(f"out {out.shape}")
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logger.info(f"speculative_logits {speculative_logits}")
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if prefill:
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next_token_logits = (
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out[batch.prefill_next_token_indices] if prefill_logprobs else out
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)
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if speculative_logits is not None:
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speculative_logits = (
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speculative_logits[batch.prefill_next_token_indices]
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if prefill_logprobs
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else speculative_logits
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)
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# next_adapter_indices = batch.adapter_meta.adapter_indices.new_empty(
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# len(batch)
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# )
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else:
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next_token_logits = out
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# next_adapter_indices = batch.adapter_meta.adapter_indices
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speculate = get_speculate()
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(
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next_input_ids,
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next_token_logprobs,
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logprobs,
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accepted_ids,
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speculative_ids,
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) = batch.next_token_chooser(
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batch.all_input_ids_tensor[:, : batch.max_seqlen],
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next_token_logits,
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speculate,
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batch.speculative_ids,
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speculative_logits,
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)
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batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
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batch.top_n_tokens, batch.top_n_tokens_tensor, logprobs, accepted_ids
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)
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if prefill:
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if len(batch) > 1 and prefill_logprobs:
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# We create the prefill_tokens_indices tensor that will be used to gather prefill logprobs
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# When batch == 1, we will just use the batch.input_ids values directly
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prefill_tokens_indices = batch.input_ids.new_zeros(len(out))
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next_position_ids = batch.position_ids.new_empty(len(batch))
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batch.slot_indices = batch.slot_indices[batch.cu_seqlen_prefill[1:] - 1]
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# We do not need cu_seqlen_prefill anymore
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batch.cu_seqlen_prefill = None
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else:
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prefill_logprobs = None
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next_position_ids = batch.position_ids
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# Cumulative length
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cumulative_length = 0
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# Results
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generations: List[Generation] = []
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stopped = True
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# Zipped iterator
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iterator = zip(batch.input_lengths, batch.all_input_ids, accepted_ids)
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# We do two for loops as the first one can run completely asynchronously from the GPU while for the second
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# one, we need to first do a GPU <-> CPU sync
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# It is faster if we delay this sync for the maximum amount of time
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# For each member of the batch
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index = 0
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for i, (input_length, all_input_ids, n_accepted_ids) in enumerate(iterator):
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# Indexing metadata
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start_index = cumulative_length
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end_index = cumulative_length + input_length
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if prefill:
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# Indexing metadata
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out_start_index = batch.prefill_cu_outlens[i]
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out_end_index = batch.prefill_cu_outlens[i + 1]
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out_length = out_end_index - out_start_index
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# Initialize position_ids
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# In decode, we do not need this as we can just increment position ids
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next_position_ids[i] = batch.position_ids[end_index - 1]
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# Initialize adapter indices
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# In decode, we only have one token per row in the batch, so grab last index
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# next_adapter_indices[i] = batch.adapter_meta.adapter_indices[
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# end_index - 1
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# ]
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# Used to gather prefill logprobs
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# Copy batch.input_ids to prefill_token_indices
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if prefill_logprobs:
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if len(batch) > 1:
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prefill_tokens_indices[out_start_index : out_end_index - 1] = (
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batch.input_ids[start_index + 1 : start_index + out_length]
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)
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else:
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# Set prefill_tokens_indices to the correct slice
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prefill_tokens_indices = batch.input_ids[
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start_index + 1 : start_index + out_length
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]
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for j in range(n_accepted_ids):
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batch.all_input_ids_tensor[i, input_length + j] = next_input_ids[index]
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index += 1
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cumulative_length += input_length
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logger.info(f"batch.input_lengths_tensor {batch.input_lengths_tensor}")
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logger.info(f"accepted_ids {accepted_ids}")
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logger.info(f"batch.all_input_ids {batch.all_input_ids}")
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# Update values
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batch.input_ids = next_input_ids[accepted_ids.cumsum(dim=-1) - 1]
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batch.speculative_ids = speculative_ids
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batch.position_ids = next_position_ids + accepted_ids
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batch.input_lengths_tensor += accepted_ids
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batch.slot_indices += accepted_ids
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# batch.adapter_meta.adapter_indices = None
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# if prefill:
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# # adjust segment lengths to account for all request lengths being 1 during decoding
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# adapter_segments, _ = find_segments(batch.adapter_meta.adapter_indices)
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# batch.adapter_meta.adapter_segments = torch.tensor(
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# adapter_segments,
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# dtype=torch.int32,
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# device=batch.adapter_meta.adapter_segments.device,
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# )
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if prefill and prefill_logprobs:
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# Get prefill logprobs
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prefill_logprobs_tensor = torch.log_softmax(out, -1)
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prefill_logprobs = torch.gather(
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prefill_logprobs_tensor, 1, prefill_tokens_indices.view(-1, 1)
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)
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# GPU <-> CPU sync
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prefill_logprobs = prefill_logprobs.view(-1).tolist()
|
||||
|
||||
# GPU <-> CPU sync
|
||||
next_token_logprobs = next_token_logprobs.tolist()
|
||||
next_token_ids = next_input_ids.tolist()
|
||||
accepted_ids = accepted_ids.tolist()
|
||||
start_decode = time.time_ns()
|
||||
|
||||
# Zipped iterator
|
||||
iterator = zip(
|
||||
batch.requests,
|
||||
batch.input_lengths,
|
||||
batch.prefix_offsets,
|
||||
batch.read_offsets,
|
||||
batch.stopping_criterias,
|
||||
batch.all_input_ids,
|
||||
batch.next_token_chooser.do_sample,
|
||||
batch.next_token_chooser.seeds,
|
||||
batch.top_n_tokens,
|
||||
accepted_ids,
|
||||
batch_top_token_ids,
|
||||
batch_top_token_logprobs,
|
||||
)
|
||||
|
||||
# For each member of the batch
|
||||
index = 0
|
||||
for i, (
|
||||
request,
|
||||
input_length,
|
||||
prefix_offset,
|
||||
read_offset,
|
||||
stopping_criteria,
|
||||
all_input_ids,
|
||||
do_sample,
|
||||
seed,
|
||||
top_n_tokens,
|
||||
n_accepted_ids,
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
) in enumerate(iterator):
|
||||
# Append next token to all tokens
|
||||
next_token_texts = []
|
||||
left = 0
|
||||
|
||||
if n_accepted_ids > 1:
|
||||
if RANK == 0:
|
||||
logger.debug(f"Speculated ids {n_accepted_ids - 1}")
|
||||
|
||||
current_stopped = False
|
||||
for j in range(index, index + n_accepted_ids):
|
||||
# Generated token
|
||||
next_token_id = next_token_ids[j]
|
||||
all_input_ids.append(next_token_id)
|
||||
next_token_text, prefix_offset, read_offset = self.decode_token(
|
||||
all_input_ids,
|
||||
prefix_offset,
|
||||
read_offset,
|
||||
)
|
||||
next_token_texts.append(next_token_text)
|
||||
|
||||
stop, reason = stopping_criteria(
|
||||
next_token_id,
|
||||
next_token_text,
|
||||
)
|
||||
|
||||
if stop:
|
||||
left = index + n_accepted_ids - j - 1
|
||||
current_stopped = True
|
||||
break
|
||||
else:
|
||||
current_stopped = False
|
||||
stopped = stopped and current_stopped
|
||||
|
||||
_next_token_ids = next_token_ids[index : index + n_accepted_ids - left]
|
||||
_next_token_logprobs = next_token_logprobs[
|
||||
index : index + n_accepted_ids - left
|
||||
]
|
||||
index += n_accepted_ids
|
||||
|
||||
# Shard generations
|
||||
# All generations will be appended in the rust sharded client
|
||||
if i % self.world_size == self.rank:
|
||||
if stop:
|
||||
# Decode generated tokens
|
||||
output_text, _, _ = self.decode_token(
|
||||
all_input_ids,
|
||||
prefix_offset=len(all_input_ids)
|
||||
- stopping_criteria.current_tokens
|
||||
- 1,
|
||||
read_offset=len(all_input_ids)
|
||||
- stopping_criteria.current_tokens,
|
||||
skip_special_tokens=True,
|
||||
)
|
||||
generated_text = GeneratedText(
|
||||
output_text,
|
||||
stopping_criteria.current_tokens,
|
||||
reason,
|
||||
seed if do_sample else None,
|
||||
)
|
||||
else:
|
||||
generated_text = None
|
||||
|
||||
# Prefill
|
||||
if prefill and request.prefill_logprobs:
|
||||
out_start_index = batch.prefill_cu_outlens[i]
|
||||
out_end_index = batch.prefill_cu_outlens[i + 1]
|
||||
|
||||
# Remove generated token to only have prefill and add nan for first prompt token
|
||||
request_prefill_logprobs = [float("nan")] + prefill_logprobs[
|
||||
out_start_index : out_end_index - 1
|
||||
]
|
||||
prefill_token_ids = all_input_ids[:-1]
|
||||
prefill_texts = self.tokenizer.batch_decode(
|
||||
prefill_token_ids,
|
||||
clean_up_tokenization_spaces=False,
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
|
||||
prefill_tokens = Tokens(
|
||||
prefill_token_ids,
|
||||
request_prefill_logprobs,
|
||||
prefill_texts,
|
||||
is_special=[],
|
||||
)
|
||||
else:
|
||||
prefill_tokens = None
|
||||
|
||||
if top_n_tokens > 0:
|
||||
all_top_tokens = []
|
||||
for top_token_ids, top_token_logprobs in zip(
|
||||
top_token_ids, top_token_logprobs
|
||||
):
|
||||
toptoken_texts = self.tokenizer.batch_decode(
|
||||
top_token_ids,
|
||||
clean_up_tokenization_spaces=False,
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
special_toptokens = [
|
||||
token_id in self.all_special_ids
|
||||
for token_id in top_token_ids
|
||||
]
|
||||
top_tokens = Tokens(
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
toptoken_texts,
|
||||
special_toptokens,
|
||||
)
|
||||
all_top_tokens.append(top_tokens)
|
||||
top_tokens = all_top_tokens
|
||||
else:
|
||||
top_tokens = None
|
||||
|
||||
generation = Generation(
|
||||
request.id,
|
||||
prefill_tokens,
|
||||
Tokens(
|
||||
_next_token_ids,
|
||||
_next_token_logprobs,
|
||||
next_token_texts,
|
||||
[nid in self.all_special_ids for nid in _next_token_ids],
|
||||
),
|
||||
generated_text,
|
||||
top_tokens,
|
||||
)
|
||||
|
||||
generations.append(generation)
|
||||
|
||||
# accept each new token for this specific request since we may
|
||||
# have more than one new token per request with speculative decoding
|
||||
for next_token_id in _next_token_ids:
|
||||
batch.next_token_chooser = (
|
||||
batch.next_token_chooser.advance_grammar_single(i, next_token_id)
|
||||
)
|
||||
|
||||
# Update values
|
||||
batch.input_lengths[i] = input_length + n_accepted_ids
|
||||
if batch.input_lengths[i] > batch.max_seqlen:
|
||||
batch.max_seqlen = batch.input_lengths[i]
|
||||
batch.prefix_offsets[i] = prefix_offset
|
||||
batch.read_offsets[i] = read_offset
|
||||
batch.all_input_ids[i] = all_input_ids
|
||||
|
||||
if stopped:
|
||||
# No need to return a batch if we know that all requests stopped
|
||||
forward_ns = start_decode - start
|
||||
decode_ns = time.time_ns() - start_decode
|
||||
return generations, None, (forward_ns, decode_ns)
|
||||
|
||||
batch.prefill_cu_outlens = None
|
||||
batch.prefill_head_indices = None
|
||||
batch.prefill_next_token_indices = None
|
||||
|
||||
forward_ns = start_decode - start
|
||||
decode_ns = time.time_ns() - start_decode
|
||||
return generations, batch, (forward_ns, decode_ns)
|
||||
|
|
@ -111,6 +111,7 @@ class FlashLlamaAttention(torch.nn.Module):
|
|||
prefix: str,
|
||||
config,
|
||||
weights,
|
||||
layer_idx,
|
||||
):
|
||||
super().__init__()
|
||||
self.num_heads = config.num_attention_heads
|
||||
|
|
@ -143,6 +144,7 @@ class FlashLlamaAttention(torch.nn.Module):
|
|||
|
||||
self.query_key_value = load_attention(config, prefix, weights, index)
|
||||
self.index = index
|
||||
self.layer_idx = layer_idx
|
||||
|
||||
o_proj = TensorParallelRowLinear.load(
|
||||
config,
|
||||
|
|
@ -163,6 +165,8 @@ class FlashLlamaAttention(torch.nn.Module):
|
|||
0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
|
||||
).repeat_interleave(self.num_groups)
|
||||
|
||||
self.step = 0
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
|
|
@ -194,6 +198,18 @@ class FlashLlamaAttention(torch.nn.Module):
|
|||
# output tensor
|
||||
attn_output = torch.empty_like(query)
|
||||
|
||||
if self.layer_idx < 4:
|
||||
torch.save(query, f"query_states_step{self.step}_layer{self.layer_idx}.pt")
|
||||
if cu_seqlen_prefill is not None:
|
||||
torch.save(
|
||||
torch.select(kv, dim=1, index=0),
|
||||
f"key_states_step{self.step}_layer{self.layer_idx}.pt",
|
||||
)
|
||||
torch.save(
|
||||
torch.select(kv, dim=1, index=1),
|
||||
f"value_states_step{self.step}_layer{self.layer_idx}.pt",
|
||||
)
|
||||
|
||||
# Prefill
|
||||
if cu_seqlen_prefill is not None:
|
||||
# flash attention
|
||||
|
|
@ -220,9 +236,14 @@ class FlashLlamaAttention(torch.nn.Module):
|
|||
max_s,
|
||||
)
|
||||
|
||||
return self.o_proj(
|
||||
attn_output.view(-1, self.num_heads * self.head_size), adapter_data
|
||||
)
|
||||
attn_output = attn_output.view(-1, self.num_heads * self.head_size)
|
||||
if self.layer_idx < 4:
|
||||
torch.save(
|
||||
attn_output, f"attn_output_step{self.step}_layer{self.layer_idx}.pt"
|
||||
)
|
||||
|
||||
self.step += 1
|
||||
return self.o_proj(attn_output, adapter_data)
|
||||
|
||||
|
||||
class LlamaMLP(nn.Module):
|
||||
|
|
@ -299,6 +320,7 @@ class LlamaMLP(nn.Module):
|
|||
def forward(self, hidden_states, adapter_data):
|
||||
if (
|
||||
SYSTEM == "rocm"
|
||||
and False
|
||||
and self.hidden_act == "silu"
|
||||
and hidden_states.shape[0] == 1
|
||||
and not self.quantize
|
||||
|
|
@ -320,13 +342,14 @@ class LlamaMLP(nn.Module):
|
|||
|
||||
|
||||
class FlashLlamaLayer(nn.Module):
|
||||
def __init__(self, index, prefix, config, weights):
|
||||
def __init__(self, index, prefix, config, weights, layer_idx):
|
||||
super().__init__()
|
||||
self.self_attn = FlashLlamaAttention(
|
||||
index=index,
|
||||
prefix=f"{prefix}.self_attn",
|
||||
config=config,
|
||||
weights=weights,
|
||||
layer_idx=layer_idx,
|
||||
)
|
||||
self.mlp = LlamaMLP(
|
||||
prefix=f"{prefix}.mlp", config=config, weights=weights, index=index
|
||||
|
|
@ -399,6 +422,7 @@ class FlashLlamaModel(torch.nn.Module):
|
|||
),
|
||||
config=config,
|
||||
weights=weights,
|
||||
layer_idx=layer_id,
|
||||
)
|
||||
for layer_id in range(config.num_hidden_layers)
|
||||
]
|
||||
|
|
|
|||
|
|
@ -1149,6 +1149,23 @@ class FlashCausalLM(Model):
|
|||
cuda_graph = None
|
||||
|
||||
if cu_seqlen_prefill is not None or cuda_graph is None:
|
||||
logger.info(f"input_ids {input_ids} {input_ids.shape}")
|
||||
logger.info(f"position_ids {position_ids} {position_ids.shape}")
|
||||
logger.info(
|
||||
f"cu_seqlen_prefill {cu_seqlen_prefill} {cu_seqlen_prefill.shape if cu_seqlen_prefill is not None else 'NONE'}"
|
||||
)
|
||||
logger.info(
|
||||
f"kv_cache {type(kv_cache)}, len={len(kv_cache)}, {len(kv_cache[0])}, shape={kv_cache[0][0].shape}"
|
||||
)
|
||||
logger.info(
|
||||
f"block_tables {type(block_tables)} {block_tables.shape} {block_tables}"
|
||||
)
|
||||
logger.info(f"slots {type(slots)} {slots.shape} {slots}")
|
||||
logger.info(f"input_lengths {input_lengths}")
|
||||
logger.info(f"max_s {max_s}")
|
||||
logger.info(f"prefill_cache_indices {batch.prefill_cache_indices}")
|
||||
logger.info(f"lm_head_indices {lm_head_indices}")
|
||||
logger.info(f"adapter_data {adapter_data}")
|
||||
logits, speculative_logits = self.model.forward(
|
||||
input_ids=input_ids,
|
||||
position_ids=position_ids,
|
||||
|
|
|
|||
Loading…
Reference in New Issue