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feat: support apple mps device #339

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feat: support apple mps device #339

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33 changes: 24 additions & 9 deletions llama/generation.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,13 @@
from llama.model import ModelArgs, Transformer
from llama.tokenizer import ChatFormat, Dialog, Message, Tokenizer

if torch.backends.mps.is_available():
device = torch.device('mps')
torch.set_default_device('mps')
elif torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')

class CompletionPrediction(TypedDict, total=False):
generation: str
Expand Down Expand Up @@ -67,16 +74,20 @@ def build(
assert 1 <= max_seq_len <= 8192, f"max_seq_len must be between 1 and 8192, got {max_seq_len}."
assert os.path.isdir(ckpt_dir), f"Checkpoint directory '{ckpt_dir}' does not exist."
assert os.path.isfile(tokenizer_path), f"Tokenizer file '{tokenizer_path}' does not exist."

if not torch.distributed.is_initialized():
torch.distributed.init_process_group("nccl")
if device == "cuda":
torch.distributed.init_process_group("nccl")
else:
torch.distributed.init_process_group("gloo")
if not model_parallel_is_initialized():
if model_parallel_size is None:
model_parallel_size = int(os.environ.get("WORLD_SIZE", 1))
initialize_model_parallel(model_parallel_size)

local_rank = int(os.environ.get("LOCAL_RANK", 0))
torch.cuda.set_device(local_rank)
if device == "cuda":
torch.cuda.set_device(local_rank)

# seed must be the same in all processes
torch.manual_seed(seed)
Expand All @@ -102,10 +113,14 @@ def build(
)
tokenizer = Tokenizer(model_path=tokenizer_path)
assert model_args.vocab_size == tokenizer.n_words
if torch.cuda.is_bf16_supported():
torch.set_default_tensor_type(torch.cuda.BFloat16Tensor)
if device == "cuda":
if torch.cuda.is_bf16_supported():
torch.set_default_tensor_type(torch.cuda.BFloat16Tensor)
else:
torch.set_default_tensor_type(torch.cuda.HalfTensor)
else:
torch.set_default_tensor_type(torch.cuda.HalfTensor)
torch.set_default_tensor_type(torch.HalfTensor)

model = Transformer(model_args)
model.load_state_dict(checkpoint, strict=False)
print(f"Loaded in {time.time() - start_time:.2f} seconds")
Expand Down Expand Up @@ -156,14 +171,14 @@ def generate(
total_len = min(params.max_seq_len, max_gen_len + max_prompt_len)

pad_id = self.tokenizer.pad_id
tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda")
tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device=device)
for k, t in enumerate(prompt_tokens):
tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device=device)
if logprobs:
token_logprobs = torch.zeros_like(tokens, dtype=torch.float)

prev_pos = 0
eos_reached = torch.tensor([False] * bsz, device="cuda")
eos_reached = torch.tensor([False] * bsz, device=device)
input_text_mask = tokens != pad_id
if min_prompt_len == total_len:
logits = self.model.forward(tokens, prev_pos)
Expand Down
27 changes: 18 additions & 9 deletions llama/model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,13 @@
)
from torch import nn

if torch.backends.mps.is_available():
device = torch.device('mps')
torch.set_default_device('mps')
elif torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')

@dataclass
class ModelArgs:
Expand Down Expand Up @@ -48,6 +55,7 @@ def forward(self, x):

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))
freqs = freqs.to(device)
t = torch.arange(end, device=freqs.device, dtype=torch.float32)
freqs = torch.outer(t, freqs)
freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
Expand Down Expand Up @@ -103,28 +111,28 @@ def __init__(self, args: ModelArgs):
bias=False,
gather_output=False,
init_method=lambda x: x,
)
).to(device)
self.wk = ColumnParallelLinear(
args.dim,
self.n_kv_heads * self.head_dim,
bias=False,
gather_output=False,
init_method=lambda x: x,
)
).to(device)
self.wv = ColumnParallelLinear(
args.dim,
self.n_kv_heads * self.head_dim,
bias=False,
gather_output=False,
init_method=lambda x: x,
)
).to(device)
self.wo = RowParallelLinear(
args.n_heads * self.head_dim,
args.dim,
bias=False,
input_is_parallel=True,
init_method=lambda x: x,
)
).to(device)

self.cache_k = torch.zeros(
(
Expand All @@ -133,15 +141,15 @@ def __init__(self, args: ModelArgs):
self.n_local_kv_heads,
self.head_dim,
)
).cuda()
).to(device)
self.cache_v = torch.zeros(
(
args.max_batch_size,
args.max_seq_len,
self.n_local_kv_heads,
self.head_dim,
)
).cuda()
).to(device)

def forward(
self,
Expand Down Expand Up @@ -257,16 +265,16 @@ def __init__(self, params: ModelArgs):

self.tok_embeddings = VocabParallelEmbedding(
params.vocab_size, params.dim, init_method=lambda x: x
)
).to(device)

self.layers = torch.nn.ModuleList()
for layer_id in range(params.n_layers):
self.layers.append(TransformerBlock(layer_id, params))
self.layers.append(TransformerBlock(layer_id, params).to(device))

self.norm = RMSNorm(params.dim, eps=params.norm_eps)
self.output = ColumnParallelLinear(
params.dim, params.vocab_size, bias=False, init_method=lambda x: x
)
).to(device)

self.freqs_cis = precompute_freqs_cis(
params.dim // params.n_heads,
Expand All @@ -276,6 +284,7 @@ def __init__(self, params: ModelArgs):

@torch.inference_mode()
def forward(self, tokens: torch.Tensor, start_pos: int):
tokens = tokens.to(device)
_bsz, seqlen = tokens.shape
h = self.tok_embeddings(tokens)
self.freqs_cis = self.freqs_cis.to(h.device)
Expand Down