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Lifting check_unitialized. (#325) 

# What does this PR do?

Lifting check_unitialized.

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This commit is contained in:
Nicolas Patry 2023-05-15 11:32:25 +02:00 committed by GitHub
parent 73d84c6ee5
commit 91e674bb85
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 11 additions and 83 deletions
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9
server/text_generation_server/models/bloom.py
View File

@ -238,15 +238,6 @@ class BLOOMSharded(BLOOM):
if name == "word_embeddings.weight":
model.lm_head._parameters["weight"] = tensor
uninitialized_parameters = []
for n, p in model.named_parameters():
if p.data.device == torch.device("meta"):
uninitialized_parameters.append(n)
if uninitialized_parameters:
raise RuntimeError(
f"found uninitialized parameters in model: {uninitialized_parameters}"
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
):

18
server/text_generation_server/models/flash_llama.py
View File

@ -139,15 +139,6 @@ class FlashLlama(FlashCausalLM):
del value
uninitialized_parameters = []
for n, p in model.named_parameters():
if p.data.device == torch.device("meta"):
uninitialized_parameters.append(n)
if uninitialized_parameters:
raise RuntimeError(
f"found uninitialized parameters in model: {uninitialized_parameters}"
)
torch.cuda.empty_cache()
model.post_load_weights(quantize)
@ -315,14 +306,5 @@ class FlashLlamaSharded(FlashLlama):
else:
module._buffers[param_name] = tensor
uninitialized_parameters = []
for n, p in model.named_parameters():
if p.data.device == torch.device("meta"):
uninitialized_parameters.append(n)
if uninitialized_parameters:
raise RuntimeError(
f"found uninitialized parameters in model: {uninitialized_parameters}"
)
torch.cuda.empty_cache()
model.post_load_weights(quantize)

9
server/text_generation_server/models/flash_neox.py
View File

@ -152,13 +152,4 @@ class FlashNeoXSharded(FlashNeoX):
else:
module._buffers[param_name] = tensor
uninitialized_parameters = []
for n, p in model.named_parameters():
if p.data.device == torch.device("meta"):
uninitialized_parameters.append(n)
if uninitialized_parameters:
raise RuntimeError(
f"found uninitialized parameters in model: {uninitialized_parameters}"
)
model.post_load_weights(quantize)

11
server/text_generation_server/models/flash_santacoder.py
View File

@ -376,17 +376,6 @@ class FlashSantacoderSharded(FlashSantacoder):
else:
module._buffers[param_name] = tensor
model.lm_head.weight = torch.nn.Parameter(model.transformer.wte.weight)
uninitialized_parameters = []
for n, p in model.named_parameters():
if p.data.device == torch.device("meta"):
uninitialized_parameters.append(n)
if uninitialized_parameters:
raise RuntimeError(
f"found uninitialized parameters in model: {uninitialized_parameters}"
)
torch.cuda.empty_cache()
model.post_load_weights(quantize)

9
server/text_generation_server/models/galactica.py
View File

@ -365,15 +365,6 @@ class GalacticaSharded(Galactica):
if name == "model.decoder.embed_tokens.weight":
model.lm_head._parameters["weight"] = tensor
uninitialized_parameters = []
for n, p in model.named_parameters():
if p.data.device == torch.device("meta"):
uninitialized_parameters.append(n)
if uninitialized_parameters:
raise RuntimeError(
f"found uninitialized parameters in model: {uninitialized_parameters}"
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
):

9
server/text_generation_server/models/gpt_neox.py
View File

@ -215,15 +215,6 @@ class GPTNeoxSharded(CausalLM):
else:
module._buffers[param_name] = tensor
uninitialized_parameters = []
for n, p in model.named_parameters():
if p.data.device == torch.device("meta"):
uninitialized_parameters.append(n)
if uninitialized_parameters:
raise RuntimeError(
f"found uninitialized parameters in model: {uninitialized_parameters}"
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
):

11
server/text_generation_server/models/model.py
View File

@ -32,6 +32,7 @@ class Model(ABC):
self.decode_buffer = decode_buffer
self.rank = rank
self.world_size = world_size
self.check_initialized()
@property
def info(self) -> InfoResponse:
@ -99,3 +100,13 @@ class Model(ABC):
return token_text, None, None
else:
return "", offset, token_offset
def check_initialized(self):
uninitialized_parameters = []
for n, p in self.model.named_parameters():
if p.data.device == torch.device("meta"):
uninitialized_parameters.append(n)
if uninitialized_parameters:
raise RuntimeError(
f"found uninitialized parameters in model {self.__class__.__name__}: {uninitialized_parameters}"
)

9
server/text_generation_server/models/opt.py
View File

@ -212,15 +212,6 @@ class OPTSharded(OPT):
if name == "model.decoder.embed_tokens.weight":
model.lm_head._parameters["weight"] = tensor
uninitialized_parameters = []
for n, p in model.named_parameters():
if p.data.device == torch.device("meta"):
uninitialized_parameters.append(n)
if uninitialized_parameters:
raise RuntimeError(
f"found uninitialized parameters in model: {uninitialized_parameters}"
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
):

9
server/text_generation_server/models/t5.py
View File

@ -222,15 +222,6 @@ class T5Sharded(Seq2SeqLM):
else:
module._buffers[param_name] = tensor
uninitialized_parameters = []
for n, p in model.named_parameters():
if p.data.device == torch.device("meta"):
uninitialized_parameters.append(n)
if uninitialized_parameters:
raise RuntimeError(
f"found uninitialized parameters in model: {uninitialized_parameters}"
)
def forward(
self,
input_ids,