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472 lines
22 KiB
472 lines
22 KiB
1 month ago
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from pathlib import Path
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from typing import List
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import json, argparse, random, time, os
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import tiktoken
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from tiktoken.load import load_tiktoken_bpe
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from extra.models.llama import Transformer, convert_from_huggingface, convert_from_gguf, fix_bf16
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from tinygrad.nn.state import safe_load, torch_load, load_state_dict, get_parameters, gguf_load
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from tinygrad import Tensor, dtypes, nn, Context, Device, GlobalCounters
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from tinygrad.helpers import Profiling, Timing, DEBUG, colored, fetch, tqdm
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class Tokenizer:
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pat_str = r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"
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def __init__(self, model_path: str):
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mergeable_ranks = load_tiktoken_bpe(model_path)
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self.num_base_tokens = len(mergeable_ranks)
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special_tokens = [
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"<|begin_of_text|>",
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"<|end_of_text|>",
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"<|reserved_special_token_0|>",
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"<|reserved_special_token_1|>",
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"<|reserved_special_token_2|>",
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"<|reserved_special_token_3|>",
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"<|start_header_id|>",
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"<|end_header_id|>",
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"<|reserved_special_token_4|>",
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"<|eot_id|>",
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] + [
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f"<|reserved_special_token_{i}|>"
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for i in range(5, 256 - 5)
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]
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self.special_tokens = {token: len(mergeable_ranks) + i for i, token in enumerate(special_tokens)}
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self.model = tiktoken.Encoding(name=model_path, pat_str=self.pat_str, mergeable_ranks=mergeable_ranks, special_tokens=self.special_tokens)
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@property
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def bos_id(self): return self.special_tokens["<|begin_of_text|>"]
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@property
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def stop_tokens(self): return {self.special_tokens["<|end_of_text|>"], self.special_tokens["<|eot_id|>"]}
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def decode(self, toks): return self.model.decode([t for t in toks if t < self.num_base_tokens])
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def encode(self, text, allow_special=False):
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return self.model.encode(text, allowed_special="all" if allow_special else set(), disallowed_special=set())
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# **** helper functions ****
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def concat_weights(models, device=None):
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def convert(name) -> Tensor:
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disk_tensors: List[Tensor] = [model[name] for model in models]
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if len(disk_tensors) == 1 or len(disk_tensors[0].shape) == 1:
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return disk_tensors[0].to(device=device)
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axis = 1 if name.endswith(".attention.wo.weight") or name.endswith(".feed_forward.w2.weight") else 0
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lazy_tensors = [data.to(device=device) for data in disk_tensors]
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return lazy_tensors[0].cat(*lazy_tensors[1:], dim=axis)
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return {name: convert(name) for name in {name: None for model in models for name in model}}
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def load(fn:str):
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if fn.endswith('.index.json'):
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with open(fn) as fp: weight_map = json.load(fp)['weight_map']
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parts = {n: load(str(Path(fn).parent / Path(n).name)) for n in set(weight_map.values())}
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return {k: parts[n][k] for k, n in weight_map.items()}
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elif fn.endswith(".gguf"):
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gguf_tensor = Tensor.empty(os.stat(fn).st_size, dtype=dtypes.uint8, device=f"disk:{fn}").to(Device.DEFAULT)
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return gguf_load(gguf_tensor)[1]
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elif fn.endswith(".safetensors"):
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return safe_load(fn)
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else:
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return torch_load(fn)
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# **** quantized linears ****
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class Int8Linear:
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def __init__(self, in_features, out_features, bias=False):
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assert bias == False
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self.weight = Tensor.ones(out_features, in_features, dtype=dtypes.int8)
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self.scale = Tensor.ones(out_features, dtype=dtypes.half)
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def __call__(self, x):
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return x.dot(self.weight.cast(dtype=dtypes.half).T*self.scale)
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@staticmethod
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def quantize(tensors, device):
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new_tensors = {}
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for name,v in tensors.items():
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if "feed_forward" in name or "attention.w" in name:
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assert "weight" in name, name
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scale = v.abs().max(axis=1) / 127.0
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int8_weight = (v.T/scale).T.cast(dtype=dtypes.int8)
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new_tensors[name] = int8_weight
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new_tensors[name.replace('weight', 'scale')] = scale
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if isinstance(device, tuple):
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new_tensors[name].shard_(device, axis=-1)
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new_tensors[name.replace('weight', 'scale')].shard_(device, axis=None)
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else:
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new_tensors[name] = v
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return new_tensors
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def NF4Linear(block_size):
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_CODE = [
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-1.0, -0.6961928009986877, -0.5250730514526367, -0.39491748809814453, -0.28444138169288635, -0.18477343022823334, -0.09105003625154495, 0.0,
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0.07958029955625534, 0.16093020141124725, 0.24611230194568634, 0.33791524171829224, 0.44070982933044434, 0.5626170039176941, 0.7229568362236023, 1.0,
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]
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CODE = Tensor.stack(*[Tensor(c, dtype=dtypes.float16) for c in _CODE])
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class _NF4Linear:
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def __init__(self, in_features, out_features, bias=False):
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assert not bias, "bias not supported"
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self.in_features, self.out_features = in_features, out_features
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self.weight = Tensor.empty(int(out_features * in_features / 2), dtype=dtypes.uint8)
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self.scale = Tensor.empty(int(out_features * in_features / block_size), 1, dtype=dtypes.float16)
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def __call__(self, x: Tensor) -> Tensor:
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high_bits = self.weight
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low_bits = (self.weight * 2 ** 4).contiguous()
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unpacked = Tensor.stack(high_bits, low_bits, dim=-1).idiv(2 ** 4)
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unscaled = CODE[unpacked].to(x.device).reshape(-1, block_size) * self.scale
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return x.linear(unscaled.reshape(self.out_features, self.in_features).T)
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@staticmethod
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def quantize(state_dict: dict[str, Tensor], device) -> dict[str, Tensor]:
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new_state_dict = {}
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for k, v in state_dict.items():
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if "feed_forward" in k or "attention.w" in k:
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grouped = v.reshape(-1, block_size)
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scale = (grouped.abs().max(axis=1, keepdim=True))
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coded = ((grouped / scale).unsqueeze(-1) - CODE.to(v.device)).abs().argmin(axis=-1).cast(dtypes.uint8).flatten()
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new_state_dict[k] = coded[::2] * 2 ** 4 + coded[1::2]
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new_state_dict[k.replace(".weight", ".scale")] = scale.cast(dtypes.float16)
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if isinstance(device, tuple):
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new_state_dict[k].shard_(device, axis=-1)
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new_state_dict[k.replace('weight', 'scale')].shard_(device, axis=None)
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else:
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new_state_dict[k] = v
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return new_state_dict
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return _NF4Linear
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MODEL_PARAMS = {
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"1B": {
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"args": {"dim": 2048, "n_heads": 32, "n_kv_heads": 8, "n_layers": 16, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 8192},
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"files": 1
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},
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"8B": {
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"args": {"dim": 4096, "n_heads": 32, "n_kv_heads": 8, "n_layers": 32, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 14336},
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"files": 1
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},
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"70B": {
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"args": {"dim": 8192, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 28672},
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"files": 8
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}
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}
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def build_transformer(model_path: Path, model_size="8B", quantize=None, device=None):
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# build model
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if quantize == "int8": linear = Int8Linear
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elif quantize == "nf4": linear = NF4Linear(64)
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else: linear = nn.Linear
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model = Transformer(**MODEL_PARAMS[model_size]["args"], linear=linear, max_context=8192, jit=True)
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# load weights
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if model_path.is_dir():
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if (model_path / "model.safetensors.index.json").exists(): weights = load(str(model_path / "model.safetensors.index.json"))
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elif (model_path / "model.safetensors").exists(): weights = load(str(model_path / "model.safetensors"))
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else: weights = concat_weights([load(str(model_path / f"consolidated.{i:02d}.pth")) for i in range(MODEL_PARAMS[model_size]["files"])], device[0] if isinstance(device, tuple) else device)
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else:
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weights = load(str(model_path))
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if "model.embed_tokens.weight" in weights:
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weights = convert_from_huggingface(weights, model, MODEL_PARAMS[model_size]["args"]["n_heads"], MODEL_PARAMS[model_size]["args"]["n_kv_heads"])
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elif "token_embd.weight" in weights:
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weights = convert_from_gguf(weights, model)
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weights = fix_bf16(weights)
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with Context(BEAM=0):
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# quantize
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if quantize == "float16": weights = {k:v.cast(quantize).contiguous() for k,v in weights.items()}
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elif quantize is not None:
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weights = linear.quantize(weights, device)
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for _,v in weights.items(): v.realize()
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# shard
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if isinstance(device, tuple):
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for k,v in nn.state.get_state_dict(model).items():
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if 'scale' in k: v.shard_(device, axis=None) # from quantized
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elif '.attention.' in k: v.shard_(device, axis=-1)
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elif '.feed_forward.w1.' in k: v.shard_(device, axis=0)
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elif '.feed_forward.w3.' in k: v.shard_(device, axis=0)
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elif '.feed_forward.' in k: v.shard_(device, axis=-1)
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elif 'tok_embeddings.weight' in k: v.shard_(device, axis=0)
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elif 'output.weight' in k: v.shard_(device, axis=0)
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else: v.shard_(device, axis=None)
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# replace weights in model
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load_state_dict(model, weights, strict=False, consume=True)
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return model
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# default settings
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TEMPERATURE = 0.95
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TOP_K = 0
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TOP_P = 0.0
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ALPHA_F = 0.0
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ALPHA_P = 0.0
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last_seen_toks = []
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def prefill(model, toks, start_pos=0):
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global last_seen_toks
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# we can skip part of the prompt if it is the same as last and start_pos=0
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if start_pos == 0:
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for i, (a, b) in enumerate(zip(toks, last_seen_toks)):
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if a != b: break
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else: i = min(len(toks), len(last_seen_toks))
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start_pos += i
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last_seen_toks = toks
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toks = toks[i:]
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# prefill the model
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for tok in tqdm(toks):
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GlobalCounters.reset()
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model(Tensor([[tok]], device=device), start_pos, TEMPERATURE, TOP_K, TOP_P, ALPHA_F, ALPHA_P).realize()
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start_pos += 1
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return start_pos
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if __name__ == "__main__":
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Tensor.no_grad = True
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parser = argparse.ArgumentParser()
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parser.add_argument("--download_model", action="store_true", help="Download a model")
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parser.add_argument("--model", type=Path, help="Model path")
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parser.add_argument("--size", choices=["1B", "8B", "70B"], default="1B", help="Model size")
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parser.add_argument("--shard", type=int, default=1, help="Shard the model across multiple devices")
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parser.add_argument("--quantize", choices=["int8", "nf4", "float16"], help="Quantization method")
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parser.add_argument("--no_api", action="store_true", help="Disable the api and run a cli test interface")
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parser.add_argument("--host", type=str, default="0.0.0.0", help="Web server bind address")
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parser.add_argument("--port", type=int, default=7776, help="Web server port")
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parser.add_argument("--debug", action="store_true", help="Enable debug mode")
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parser.add_argument("--seed", type=int, help="Random seed")
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parser.add_argument("--temperature", type=int, default=0.85, help="Temperature")
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parser.add_argument("--benchmark", action="store_true", help="Run a benchmark")
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parser.add_argument("--timing", action="store_true", help="Print timing per token")
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parser.add_argument("--profile", action="store_true", help="Output profile data")
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args = parser.parse_args()
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# download_model is the default without a model passed in
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if args.download_model or not args.model:
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if args.size == "1B":
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fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model", "tokenizer.model", subdir="llama3-1b-instruct")
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args.model = fetch("https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q6_K.gguf", "Llama-3.2-1B-Instruct-Q6_K.gguf", subdir="llama3-1b-instruct")
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elif args.size == "8B":
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fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model", "tokenizer.model", subdir="llama3-8b-sfr")
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fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/resolve/main/model-00001-of-00004.safetensors", "model-00001-of-00004.safetensors", subdir="llama3-8b-sfr")
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fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/resolve/main/model-00002-of-00004.safetensors", "model-00002-of-00004.safetensors", subdir="llama3-8b-sfr")
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fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/resolve/main/model-00003-of-00004.safetensors", "model-00003-of-00004.safetensors", subdir="llama3-8b-sfr")
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fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/resolve/main/model-00004-of-00004.safetensors", "model-00004-of-00004.safetensors", subdir="llama3-8b-sfr")
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args.model = fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/raw/main/model.safetensors.index.json", "model.safetensors.index.json", subdir="llama3-8b-sfr")
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elif args.size == "70B":
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subdir = "Llama-3.1-Nemotron-70B-Instruct-HF"
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args.model = fetch("https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF/resolve/main/model.safetensors.index.json?download=true", "model.safetensors.index.json", subdir=subdir)
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fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model", "tokenizer.model", subdir=subdir)
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for i in range(30):
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fetch(f"https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF/resolve/main/model-{i+1:05d}-of-00030.safetensors?download=true", f"model-{i+1:05d}-of-00030.safetensors", subdir=subdir)
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assert args.model is not None, "please provide --model option"
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if args.seed is not None: Tensor.manual_seed(args.seed)
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if args.benchmark: Tensor.manual_seed(42)
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print(f"seed = {Tensor._seed}")
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TEMPERATURE = args.temperature
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tokenizer = Tokenizer(str((args.model if args.model.is_dir() else args.model.parent) / "tokenizer.model"))
|
||
|
|
def encode_role(role: str):
|
||
|
|
return [tokenizer.special_tokens["<|start_header_id|>"]] + tokenizer.encode(role) + [tokenizer.special_tokens["<|end_header_id|>"]] + tokenizer.encode("\n\n")
|
||
|
|
def encode_message(role: str, content: str):
|
||
|
|
return encode_role(role) + tokenizer.encode(content.strip()) + [tokenizer.special_tokens["<|eot_id|>"]]
|
||
|
|
|
||
|
|
device = tuple(f"{Device.DEFAULT}:{i}" for i in range(args.shard)) if args.shard > 1 else Device.DEFAULT
|
||
|
|
model = build_transformer(args.model, model_size=args.size, quantize=args.quantize, device=device)
|
||
|
|
param_bytes = sum(x.lazydata.size * x.dtype.itemsize for x in get_parameters(model))
|
||
|
|
|
||
|
|
if not args.no_api and not args.benchmark:
|
||
|
|
from bottle import Bottle, request, response, HTTPResponse, abort, static_file
|
||
|
|
app = Bottle()
|
||
|
|
|
||
|
|
cors_headers = {
|
||
|
|
"Access-Control-Allow-Origin": "*",
|
||
|
|
"Access-Control-Allow-Methods": "GET, POST, PUT, DELETE, OPTIONS",
|
||
|
|
"Access-Control-Allow-Headers": "Origin, Accept, Content-Type, X-Requested-With, X-CSRF-Token, Authorization",
|
||
|
|
"Access-Control-Allow-Credentials": "true",
|
||
|
|
}
|
||
|
|
@app.hook("before_request")
|
||
|
|
def handle_options():
|
||
|
|
if request.method == "OPTIONS": raise HTTPResponse(headers=cors_headers)
|
||
|
|
@app.hook("after_request")
|
||
|
|
def enable_cors():
|
||
|
|
for key, value in cors_headers.items(): response.set_header(key, value)
|
||
|
|
|
||
|
|
@app.route("/<filename>")
|
||
|
|
def server_static(filename): return static_file(filename, root=(Path(__file__).parent / "tinychat").as_posix())
|
||
|
|
@app.route("/assets/<filename:path>")
|
||
|
|
def server_assets(filename): return static_file(filename, root=(Path(__file__).parent / "tinychat" / "assets").as_posix())
|
||
|
|
@app.route("/")
|
||
|
|
def index():
|
||
|
|
return static_file("index.html", root=(Path(__file__).parent / "tinychat").as_posix())
|
||
|
|
|
||
|
|
@app.get("/v1/models")
|
||
|
|
def models():
|
||
|
|
return json.dumps([str(args.model)])
|
||
|
|
|
||
|
|
@app.post("/v1/internal/token-count")
|
||
|
|
def token_count():
|
||
|
|
rjson = json.loads(request.body.read())
|
||
|
|
return json.dumps(len(tokenizer.encode(rjson.get("text", ""))))
|
||
|
|
@app.post("/v1/token/encode")
|
||
|
|
def token_encode():
|
||
|
|
rjson = json.loads(request.body.read())
|
||
|
|
return json.dumps(tokenizer.encode(rjson.get("text", "")))
|
||
|
|
|
||
|
|
@app.post("/v1/completions")
|
||
|
|
def completions():
|
||
|
|
rjson = json.loads(request.body.read())
|
||
|
|
|
||
|
|
# check if we are streaming
|
||
|
|
if rjson.get("stream", False):
|
||
|
|
response.content_type = "text/event-stream"
|
||
|
|
response.set_header("Cache-Control", "no-cache")
|
||
|
|
else: abort(400, "streaming required")
|
||
|
|
|
||
|
|
toks = [tokenizer.bos_id] + tokenizer.encode(rjson.get("prompt", ""), allow_special=True)
|
||
|
|
|
||
|
|
start_pos = prefill(model, toks[:-1])
|
||
|
|
last_tok = toks[-1]
|
||
|
|
while True:
|
||
|
|
GlobalCounters.reset()
|
||
|
|
tok = model(Tensor([[last_tok]], device=device), start_pos, TEMPERATURE, TOP_K, TOP_P, ALPHA_F, ALPHA_P).item()
|
||
|
|
start_pos += 1
|
||
|
|
last_tok = tok
|
||
|
|
if tok in tokenizer.stop_tokens: break
|
||
|
|
|
||
|
|
res = {
|
||
|
|
"choices": [{
|
||
|
|
"text": tokenizer.decode([tok]),
|
||
|
|
}]
|
||
|
|
}
|
||
|
|
yield f"data: {json.dumps(res)}\n\n"
|
||
|
|
|
||
|
|
@app.post("/v1/chat/token/encode")
|
||
|
|
def chat_token_encode():
|
||
|
|
rjson = json.loads(request.body.read())
|
||
|
|
if "messages" not in rjson: abort(400, "messages required")
|
||
|
|
toks = [tokenizer.bos_id]
|
||
|
|
for message in rjson["messages"]:
|
||
|
|
toks += encode_message(message["role"], message["content"])
|
||
|
|
if len(rjson["messages"]) > 0 and message["role"] == "user":
|
||
|
|
toks += encode_role("assistant")
|
||
|
|
return json.dumps(toks)
|
||
|
|
|
||
|
|
@app.post("/v1/chat/completions")
|
||
|
|
def chat_completions():
|
||
|
|
global last_seen_toks
|
||
|
|
rjson = json.loads(request.body.read())
|
||
|
|
if "messages" not in rjson: abort(400, "messages required")
|
||
|
|
|
||
|
|
# check if we are streaming
|
||
|
|
if rjson.get("stream", False):
|
||
|
|
response.content_type = "text/event-stream"
|
||
|
|
response.set_header("Cache-Control", "no-cache")
|
||
|
|
else: abort(400, "streaming required")
|
||
|
|
|
||
|
|
toks = [tokenizer.bos_id]
|
||
|
|
for message in rjson["messages"]:
|
||
|
|
toks += encode_message(message["role"], message["content"])
|
||
|
|
# ensure that the last message was a user message
|
||
|
|
if message["role"] != "user": abort(400, "last message must be a user message")
|
||
|
|
toks += encode_role("assistant")
|
||
|
|
|
||
|
|
random_id = random.randbytes(16).hex()
|
||
|
|
|
||
|
|
start_pos = prefill(model, toks[:-1])
|
||
|
|
last_tok = toks[-1]
|
||
|
|
last_seen_toks.append(last_tok)
|
||
|
|
while True:
|
||
|
|
GlobalCounters.reset()
|
||
|
|
tok = model(Tensor([[last_tok]], device=device), start_pos, TEMPERATURE, TOP_K, TOP_P, ALPHA_F, ALPHA_P).item()
|
||
|
|
start_pos += 1
|
||
|
|
last_tok = tok
|
||
|
|
last_seen_toks.append(tok)
|
||
|
|
if tok in tokenizer.stop_tokens: break
|
||
|
|
|
||
|
|
res = {
|
||
|
|
"id": random_id,
|
||
|
|
"object": "chat.completion.chunk",
|
||
|
|
"created": int(time.time()),
|
||
|
|
"model": str(args.model),
|
||
|
|
"choices": [{
|
||
|
|
"index": 0,
|
||
|
|
"delta": {
|
||
|
|
"role": "assistant",
|
||
|
|
"content": tokenizer.decode([tok]),
|
||
|
|
},
|
||
|
|
"finish_reason": None,
|
||
|
|
}]
|
||
|
|
}
|
||
|
|
yield f"data: {json.dumps(res)}\n\n"
|
||
|
|
|
||
|
|
res = {
|
||
|
|
"id": random_id,
|
||
|
|
"object": "chat.completion.chunk",
|
||
|
|
"created": int(time.time()),
|
||
|
|
"model": str(args.model),
|
||
|
|
"choices": [{
|
||
|
|
"index": 0,
|
||
|
|
"delta": {},
|
||
|
|
"finish_reason": "stop",
|
||
|
|
}]
|
||
|
|
}
|
||
|
|
yield f"data: {json.dumps(res)}\n\n"
|
||
|
|
|
||
|
|
app.run(host=args.host, port=args.port, debug=args.debug)
|
||
|
|
elif args.benchmark:
|
||
|
|
toks = [tokenizer.bos_id] + encode_message("user", "Hello.") + encode_role("assistant")
|
||
|
|
|
||
|
|
start_pos = prefill(model, toks[:-1])
|
||
|
|
last_tok = toks[-1]
|
||
|
|
generated = ""
|
||
|
|
for _ in range(20):
|
||
|
|
GlobalCounters.reset()
|
||
|
|
st = GlobalCounters.time_sum_s
|
||
|
|
with Profiling(enabled=args.profile):
|
||
|
|
with Timing("total ", on_exit=lambda x: f", {1e9/x:.2f} tok/s, {GlobalCounters.global_mem/x:.2f} GB/s, param {param_bytes/x:.2f} GB/s"):
|
||
|
|
with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU" if DEBUG>=2 else "")+
|
||
|
|
f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB"+
|
||
|
|
(f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s, param {param_bytes*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None):
|
||
|
|
tok = model(Tensor([[last_tok]], device=device), start_pos, TEMPERATURE, TOP_K, TOP_P, ALPHA_F, ALPHA_P)
|
||
|
|
tok = tok.item()
|
||
|
|
start_pos += 1
|
||
|
|
last_tok = tok
|
||
|
|
generated += tokenizer.decode([tok])
|
||
|
|
print(generated)
|
||
|
|
if "LLaMA-3/8B-SF-DPO" in args.model.as_posix() and (TEMPERATURE == 0.85 or TEMPERATURE == 0):
|
||
|
|
if TEMPERATURE == 0.85:
|
||
|
|
EXPECTED_TEXT = {
|
||
|
|
1: "Hello! How can I help you today? If you have any questions or need assistance with anything,",
|
||
|
|
2: "Hello! How can I help you today? If you have any questions, need assistance or just want",
|
||
|
|
3: "Hello! How can I help you today? If you have any questions or need assistance, feel free",
|
||
|
|
4: "Hello! How can I assist you today? If you have any questions, need information, or require",
|
||
|
|
5: "Hello! How can I assist you today? If you have any questions or need help with something",
|
||
|
|
6: "Hello! How can I assist you today? If you have any questions, need information, or require",
|
||
|
|
}
|
||
|
|
else:
|
||
|
|
EXPECTED_TEXT = {k: "Hello! How can I assist you today? If you have any questions or need help with something," for k in range(1, 7)}
|
||
|
|
assert generated == EXPECTED_TEXT[args.shard], f"{generated=} {EXPECTED_TEXT[args.shard]}"
|
||
|
|
print("\n" + colored("output validated", "green")) # NOTE: "\n" inside colored does not render the color in github action
|
||
|
|
else:
|
||
|
|
prompt = [tokenizer.bos_id] + encode_message("system", "You are an helpful assistant.")
|
||
|
|
|
||
|
|
start_pos = prefill(model, prompt)
|
||
|
|
while True:
|
||
|
|
toks = encode_message("user", input("Q: ")) + encode_role("assistant")
|
||
|
|
|
||
|
|
start_pos = prefill(model, toks[:-1], start_pos=start_pos)
|
||
|
|
last_tok = toks[-1]
|
||
|
|
while True:
|
||
|
|
GlobalCounters.reset()
|
||
|
|
if args.timing or args.profile: print("")
|
||
|
|
st = GlobalCounters.time_sum_s
|
||
|
|
with Profiling(enabled=args.profile):
|
||
|
|
with Timing("total ", enabled=args.timing, on_exit=lambda x: f", {1e9/x:.2f} tok/s, {GlobalCounters.global_mem/x:.2f} GB/s, param {param_bytes/x:.2f} GB/s"):
|
||
|
|
with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU" if DEBUG>=2 else "")+
|
||
|
|
f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB"+
|
||
|
|
(f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s, param {param_bytes*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None, enabled=args.timing):
|
||
|
|
|
||
|
|
tok = model(Tensor([[last_tok]], device=device), start_pos, TEMPERATURE, TOP_K, TOP_P, ALPHA_F, ALPHA_P)
|
||
|
|
tok = tok.item()
|
||
|
|
start_pos += 1
|
||
|
|
last_tok = tok
|
||
|
|
if tok in tokenizer.stop_tokens: break
|
||
|
|
print(tokenizer.decode([tok]), end="", flush=True)
|
||
|
|
print(flush=True)
|