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[NPU] update fused layers for GW #12459

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merged 4 commits into from
Nov 28, 2024
Merged

[NPU] update fused layers for GW #12459

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06bd01e
update fused layers for GW
rnwang04 Nov 28, 2024
2f86833
fix
rnwang04 Nov 28, 2024
7af67fb
fix llama condition for glm model
rnwang04 Nov 28, 2024
1e004ae
update
rnwang04 Nov 28, 2024
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6 changes: 5 additions & 1 deletion python/llm/src/ipex_llm/transformers/npu_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -136,6 +136,7 @@ def from_pretrained(cls, *args, **kwargs):
mock_device = kwargs.pop('device', None) # For mock on CPU
convert_model = kwargs.pop('convert_model', False)
save_directory = kwargs.pop('save_directory', None)
fuse_layers = kwargs.pop('fuse_layers', None)

invalidInputError(
quantization_group_size in [0, 32, 64, 128],
Expand Down Expand Up @@ -204,6 +205,7 @@ def from_pretrained(cls, *args, **kwargs):
"transpose_value_cache": transpose_value_cache,
"convert_model": convert_model,
"save_directory": save_directory,
"fuse_layers": fuse_layers
}
model = cls.optimize_npu_model(*args, **optimize_kwargs)
else:
Expand Down Expand Up @@ -243,6 +245,7 @@ def optimize_npu_model(cls, *args, **kwargs):
transpose_value_cache = kwargs.pop("transpose_value_cache", True)
convert_model = kwargs.pop('convert_model', False)
save_directory = kwargs.pop('save_directory', None)
fuse_layers = kwargs.pop('fuse_layers', None)

if hasattr(model, "llm"):
llm = model.llm
Expand Down Expand Up @@ -282,7 +285,8 @@ def optimize_npu_model(cls, *args, **kwargs):
group_size=quantization_group_size,
qtype=qtype,
convert_model=convert_model,
save_directory=save_directory)
save_directory=save_directory,
fuse_layers=fuse_layers)
model.save_low_bit = types.MethodType(save_low_bit, model)
return model

Expand Down
54 changes: 37 additions & 17 deletions python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -195,7 +195,8 @@ def convert_llm(model: torch.nn.Module,
group_size: int,
qtype: str,
convert_model: bool=False,
save_directory: str=None):
save_directory: str=None,
fuse_layers: int=None):
# whether to set layernorm weight as const
layernorm_const = os.environ.get("IPEX_LLM_NPU_LAYERNORM_CONST", "1") == "1"
if group_size == 0:
Expand All @@ -216,7 +217,8 @@ def convert_llm(model: torch.nn.Module,
n_splits_linear,
n_splits_down_proj,
group_size,
save_directory)
save_directory,
fuse_layers=fuse_layers)
return 0
if model.config.model_type == "llama":
with tempfile.TemporaryDirectory() as temp_dir:
Expand Down Expand Up @@ -422,18 +424,22 @@ def convert_llm_for_deploy(model: torch.nn.Module,
n_splits_linear: int,
n_splits_down_proj: int,
group_size: int,
save_directory: str=None):
save_directory: str=None,
fuse_layers: int=None):
os.mkdir(save_directory)
weight_dir = os.path.join(save_directory, "model_weights")
os.mkdir(weight_dir)
layernorm_const = os.environ.get("IPEX_LLM_NPU_LAYERNORM_CONST", "1") == "1"

if model.config.model_type == "qwen2":
if model.config.hidden_size == 1536:
# Qwen2-1.5B-Instruct
fused_layers = 1
if group_size == 0:
if model.config.hidden_size == 1536:
# Qwen2-1.5B-Instruct
fused_layers = 1 if fuse_layers is None else fuse_layers
else:
fused_layers = 2 if fuse_layers is None else fuse_layers
else:
fused_layers = 2
fused_layers = len(model.model.layers) if fuse_layers is None else fuse_layers
update_dict = {"kv_len": kv_len,
"num_head": model.model.layers[0].self_attn.num_heads,
"head_dim": model.model.layers[0].self_attn.head_dim,
Expand Down Expand Up @@ -469,20 +475,31 @@ def convert_llm_for_deploy(model: torch.nn.Module,
embedding_post = False
cos_sin_input = False
use_prefill_sdp = False
if model.config.vocab_size == 32000:
# for Llama2-7B
fused_layers = 4
use_prefill_sdp = True
else:
if model.config.intermediate_size == 8192:
if group_size == 0:
if model.config.intermediate_size == 11008:
# for Llama2-7B
fused_layers = 4 if fuse_layers is None else fuse_layers
use_prefill_sdp = True
elif model.config.intermediate_size == 14336:
# for Llama3-8B
fused_layers = 2 if fuse_layers is None else fuse_layers
use_prefill_sdp = True
elif not hasattr(model.model.layers[0].self_attn.rotary_emb, "cos_cached"):
# llama3.2 1B & # llama3.2 3B
embedding_post = True
cos_sin_input = True
fused_layers = 2
fused_layers = 2 if fuse_layers is None else fuse_layers
else:
# for Llama3-8B
fused_layers = 2
fused_layers = 2 if fuse_layers is None else fuse_layers
else:
if model.config.intermediate_size in [11008, 14336]:
# for Llama2-7B & Llama3-8B
use_prefill_sdp = True
elif not hasattr(model.model.layers[0].self_attn.rotary_emb, "cos_cached"):
# llama3.2 1B & # llama3.2 3B
embedding_post = True
cos_sin_input = True
fused_layers = len(model.model.layers) if fuse_layers is None else fuse_layers
update_dict = {"kv_len": kv_len,
"num_head": model.model.layers[0].self_attn.num_heads,
"head_dim": model.model.layers[0].self_attn.head_dim,
Expand Down Expand Up @@ -518,7 +535,10 @@ def convert_llm_for_deploy(model: torch.nn.Module,
save_directory, weight_dir, transpose_value_cache, max_prompt_len,
group_size, layernorm_const, "prefill")
elif model.config.model_type == "minicpm":
fused_layers = 4
if group_size == 0:
fused_layers = 4 if fuse_layers is None else fuse_layers
else:
fused_layers = len(model.model.layers) if fuse_layers is None else fuse_layers
update_dict = {"kv_len": kv_len,
"num_head": model.model.layers[0].self_attn.num_heads,
"head_dim": model.model.layers[0].self_attn.head_dim,
Expand Down