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llama/__init__.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the GNU General Public License version 3.
+
+from .generation import LLaMA
+from .model import ModelArgs, Transformer
+from .tokenizer import Tokenizer

llama/generation.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the GNU General Public License version 3.
+
+from typing import List
+
+import torch
+import traceback
+
+from llama.tokenizer import Tokenizer
+from llama.model import Transformer
+from tqdm import trange
+
+
+class LLaMA:
+    def __init__(self, model: Transformer, tokenizer: Tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    def generate(
+            self,
+            prompts: List[str],
+            max_gen_len: int,
+            temperature: float = 0.8,
+            top_p: float = 0.95,
+    ) -> List[str]:
+        bsz = len(prompts)
+        params = self.model.params
+        assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
+
+        count_newlines = prompts[0].count("\n")
+
+        prompt_tokens = [self.tokenizer.encode(x, bos=True, eos=False) for x in prompts]
+
+        min_prompt_size = min([len(t) for t in prompt_tokens])
+        max_prompt_size = max([len(t) for t in prompt_tokens])
+
+        total_len = min(params.max_seq_len, max_gen_len + max_prompt_size)
+
+        tokens = torch.full((bsz, total_len), self.tokenizer.pad_id).long()
+        for k, t in enumerate(prompt_tokens):
+            tokens[k, : len(t)] = torch.tensor(t).long()
+            tokens[k, -1] = self.tokenizer.eos_id
+        input_text_mask = tokens != self.tokenizer.pad_id
+        start_pos = min_prompt_size
+        prev_pos = 0
+        decoded = [None] * bsz
+        for cur_pos in trange(start_pos, total_len, desc="forward"):
+            logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
+            if temperature > 0:
+                probs = torch.softmax(logits / temperature, dim=-1)
+                next_token = sample_top_p(probs, top_p)
+            else:
+                next_token = torch.argmax(logits, dim=-1)
+            next_token = next_token.reshape(-1).cpu()
+            # only replace token if prompt has already been generated
+            next_token = torch.where(
+                input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
+            )
+            tokens[:, cur_pos] = next_token
+            prev_pos = cur_pos
+
+            print("-" * 30)
+            for i, t in enumerate(tokens.tolist()):
+                # i = cur_pos
+                # t = next_token
+                # cut to max gen len
+                # t = t[: len(pr-ompt_tokens[i]) + max_gen_len]
+                t = t[: min(cur_pos, len(prompt_tokens[i]) + max_gen_len)]
+                # cut to eos tok if any
+                try:
+                    t = t[: t.index(self.tokenizer.eos_id)]
+                except ValueError:
+                    pass  # traceback.print_exc()
+                try:
+                    d = self.tokenizer.decode(t)
+                    print([i] * 20)
+                    print(d)
+                    decoded[i] = d
+
+                    result_count_newlines = d.count("\n")
+                    if result_count_newlines > count_newlines:
+                        return decoded
+
+                except IndexError:
+                    traceback.print_exc()
+                    print(t)
+            print("-" * 30)
+        return decoded
+
+
+def sample_top_p(probs, p):
+    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
+    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    mask = probs_sum - probs_sort > p
+    probs_sort[mask] = 0.0
+    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
+    next_token = torch.multinomial(probs_sort, num_samples=1)
+    next_token = torch.gather(probs_idx, -1, next_token)
+    return next_token

llama/model.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the GNU General Public License version 3.
+
+from typing import Optional, Tuple
+from dataclasses import dataclass
+import math
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.nn.utils import skip_init
+
+from tqdm import tqdm
+
+@dataclass
+class ModelArgs:
+    dim: int = 512
+    n_layers: int = 8
+    n_heads: int = 8
+    vocab_size: int = -1  # defined later by tokenizer
+    multiple_of: int = 256  # make SwiGLU hidden layer size multiple of large power of 2
+    norm_eps: float = 1e-5
+
+    max_batch_size: int = 32
+    max_seq_len: int = 1024
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return freqs_cis
+
+
+def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
+    ndim = x.ndim
+    assert 0 <= 1 < ndim
+    assert freqs_cis.shape == (x.shape[1], x.shape[-1])
+    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+    return freqs_cis.view(*shape)
+
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        self.n_local_heads = args.n_heads  # // fs_init.get_model_parallel_world_size()
+        self.head_dim = args.dim // args.n_heads
+
+        self.wq = skip_init(nn.Linear,
+            args.dim,
+            args.n_heads * self.head_dim,
+            bias=False,
+        )
+        self.wk = skip_init(nn.Linear,
+            args.dim,
+            args.n_heads * self.head_dim,
+            bias=False,
+        )
+        self.wv = skip_init(nn.Linear,
+            args.dim,
+            args.n_heads * self.head_dim,
+            bias=False,
+        )
+        self.wo = skip_init(nn.Linear,
+            args.n_heads * self.head_dim,
+            args.dim,
+            bias=False,
+        )
+
+        self.cache_k = torch.zeros(
+            (args.max_batch_size, args.max_seq_len, self.n_local_heads, self.head_dim)
+        ).cuda()
+        self.cache_v = torch.zeros(
+            (args.max_batch_size, args.max_seq_len, self.n_local_heads, self.head_dim)
+        ).cuda()
+
+    def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]):
+        bsz, seqlen, _ = x.shape
+        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
+
+        xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        xk = xk.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        xv = xv.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+
+        xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)
+
+        self.cache_k = self.cache_k.to(xq)
+        self.cache_v = self.cache_v.to(xq)
+
+        self.cache_k[:bsz, start_pos : start_pos + seqlen] = xk
+        self.cache_v[:bsz, start_pos : start_pos + seqlen] = xv
+
+        keys = self.cache_k[:bsz, : start_pos + seqlen]
+        values = self.cache_v[:bsz, : start_pos + seqlen]
+
+        xq = xq.transpose(1, 2)
+        keys = keys.transpose(1, 2)
+        values = values.transpose(1, 2)
+        scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
+        if mask is not None:
+            scores = scores + mask  # (bs, n_local_heads, slen, cache_len + slen)
+        scores = F.softmax(scores.float(), dim=-1).type_as(xq)
+        output = torch.matmul(scores, values)  # (bs, n_local_heads, slen, head_dim)
+        output = output.transpose(
+            1, 2
+        ).contiguous().view(bsz, seqlen, -1)
+
+        return self.wo(output)
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int,
+    ):
+        super().__init__()
+        hidden_dim = int(2 * hidden_dim / 3)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = skip_init(nn.Linear,
+            dim,
+            hidden_dim,
+            bias=False,
+        )
+        self.w2 = skip_init(nn.Linear,
+            hidden_dim,
+            dim,
+            bias=False,
+        )
+        self.w3 = skip_init(nn.Linear,
+            dim,
+            hidden_dim,
+            bias=False,
+        )
+
+    def forward(self, x):
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, layer_id: int, args: ModelArgs):
+        super().__init__()
+        self.n_heads = args.n_heads
+        self.dim = args.dim
+        self.head_dim = args.dim // args.n_heads
+        self.attention = Attention(args)
+        self.feed_forward = FeedForward(
+            dim=args.dim, hidden_dim=4 * args.dim, multiple_of=args.multiple_of
+        )
+        self.layer_id = layer_id
+        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
+        self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
+
+    def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]):
+        h = x + self.attention.forward(self.attention_norm(x), start_pos, freqs_cis, mask)
+        out = h + self.feed_forward.forward(self.ffn_norm(h))
+        return out
+
+# https://github.com/gmorenz/llama/commit/4daf7f1a2f2bb22208b5d464bc2a18511d54408d
+def move_parameters_to_gpu(module):
+    if not hasattr(module, "saved"):
+        module.saved = module._parameters.copy()
+    for k, param in module.saved.items():
+        if param is not None:
+            module._parameters[k] = param.to("cuda", non_blocking=True)
+    for child in module.children():
+        move_parameters_to_gpu(child)
+
+def move_parameters_to_cpu(module):
+    for k, param in module.saved.items():
+        del module._parameters[k]
+        module._parameters[k] = param
+    for child in module.children():
+        move_parameters_to_cpu(child)
+
+
+class Transformer(nn.Module):
+    def __init__(self, params: ModelArgs):
+        super().__init__()
+        self.params = params
+        self.vocab_size = params.vocab_size
+        self.n_layers = params.n_layers
+
+        self.tok_embeddings = skip_init(nn.Embedding,
+            params.vocab_size,
+            params.dim,
+        )
+
+        self.layers = torch.nn.ModuleList()
+        for layer_id in range(params.n_layers):
+            self.layers.append(TransformerBlock(layer_id, params))
+
+        self.layer_locations = [None] * len(self.layers)
+
+        self.norm = RMSNorm(params.dim, eps=params.norm_eps).cuda()
+        self.output = skip_init(nn.Linear,
+            params.dim,
+            params.vocab_size,
+            bias=False,
+        ).cuda()
+
+        self.freqs_cis = precompute_freqs_cis(
+            self.params.dim // self.params.n_heads, self.params.max_seq_len * 2
+        ).cuda()
+
+    @torch.inference_mode()
+    def forward(self, tokens: torch.Tensor, start_pos: int):
+        use_gpu = True  # start_pos == 0
+
+        _bsz, seqlen = tokens.shape
+        h = self.tok_embeddings(tokens)
+        self.freqs_cis = self.freqs_cis
+        freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]
+        if use_gpu:
+            h = h.cuda()
+
+        mask = None
+        if seqlen > 1:
+            mask = torch.full(
+                (1, 1, seqlen, seqlen), float("-inf"), device=tokens.device
+            )
+            mask = torch.triu(mask, diagonal=start_pos + 1).type_as(h)
+
+        if use_gpu and mask is not None:
+            mask = mask.cuda()
+
+        for layer in tqdm(self.layers, desc="flayers", leave=True):
+            if use_gpu:
+                move_parameters_to_gpu(layer)
+            h = layer(h, start_pos, freqs_cis, mask)
+            if use_gpu:
+                move_parameters_to_cpu(layer)
+
+        h = self.norm(h)
+        if use_gpu:
+            del mask
+            torch.cuda.empty_cache()
+        output = self.output(h[:, -1, :])  # only compute last logits
+        return output.float()

llama/tokenizer.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the GNU General Public License version 3.
+
+from sentencepiece import SentencePieceProcessor
+from logging import getLogger
+from typing import List
+import os
+
+
+logger = getLogger()
+
+
+class Tokenizer:
+    def __init__(self, model_path: str):
+        # reload tokenizer
+        assert os.path.isfile(model_path), model_path
+        self.sp_model = SentencePieceProcessor(model_file=model_path)
+        logger.info(f"Reloaded SentencePiece model from {model_path}")
+
+        # BOS / EOS token IDs
+        self.n_words: int = self.sp_model.vocab_size()
+        self.bos_id: int = self.sp_model.bos_id()
+        self.eos_id: int = self.sp_model.eos_id()
+        self.pad_id: int = self.sp_model.pad_id()
+        logger.info(
+            f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
+        )
+        assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
+
+    def encode(self, s: str, bos: bool, eos: bool) -> List[int]:
+        assert type(s) is str
+        t = self.sp_model.encode(s)
+        if bos:
+            t = [self.bos_id] + t
+        if eos:
+            t = t + [self.eos_id]
+        return t
+
+    def decode(self, t: List[int]) -> str:
+        return self.sp_model.decode(t)