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Support loading 4 bit Qwen2 #1476

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Merged
regisss merged 2 commits into from
Nov 18, 2024
Merged

Support loading 4 bit Qwen2 #1476

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67e16b3
Support loading 4bit Qwen2
mengniwang95 Nov 11, 2024
f2b33ed
fix CI
mengniwang95 Nov 17, 2024
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23 changes: 19 additions & 4 deletions optimum/habana/transformers/models/qwen2/modeling_qwen2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -198,9 +198,22 @@ def __init__(self, config: Qwen2Config, layer_idx: Optional[int] = None):
self.block_size = 4096
self.rotary_emb = GaudiRotaryEmbedding(config=self.config)

def get_k_proj_weight(self):
"""4bit quantization in GPTQ replaces the k_proj.weight with qweight."""
if hasattr(self.k_proj, "qweight"):
return self.k_proj.qweight
return self.k_proj.weight

def get_k_proj_weight_dtype(self):
"""4bit quantization in GPTQ replaces the k_proj.weight with qweight.
Scales tensor gets the weight dtype."""
if hasattr(self.k_proj, "qweight"):
return self.k_proj.scales.dtype
return self.k_proj.weight.dtype

def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
cache_shape = (batch_size, self.num_key_value_heads, max_seq_len, self.head_dim)
device = self.k_proj.weight.device
device = self.get_k_proj_weight().device
dtype = self.config.torch_dtype
self.k_cache.allocate(inp_seq_len, dtype, device, cache_shape)
self.v_cache.allocate(inp_seq_len, dtype, device, cache_shape)
Expand All @@ -211,7 +224,7 @@ def update_sincos_cache(self, seq_len):
# reduce memory consumption and improve performance.
if seq_len > self.max_position_embeddings:
self.max_position_embeddings = seq_len
_, _ = self.rotary_emb(self.k_proj.weight, seq_len=seq_len)
_, _ = self.rotary_emb(self.get_k_proj_weight(), seq_len=seq_len)

def reorder(self, tensor, beam_idx, dim_a, dim_b):
updated = tensor.index_select(0, beam_idx)
Expand Down Expand Up @@ -316,9 +329,11 @@ def pre_attn_forward(
past_key_value = (self.k_cache.get_shape(), self.v_cache.get_shape())
else:
if past_key_value is None:
past_key = torch.zeros(key_states.shape, dtype=self.k_proj.weight.dtype, device=key_states.device)
past_key = torch.zeros(
key_states.shape, dtype=self.get_k_proj_weight_dtype(), device=key_states.device
)
past_value = torch.zeros(
key_states.shape, dtype=self.k_proj.weight.dtype, device=key_states.device
key_states.shape, dtype=self.get_k_proj_weight_dtype(), device=key_states.device
)
past_key_value = [past_key, past_value]
key_states = self.k_cache.update(past_key_value[0], key_states, 2, token_idx, self.inp_seq_len)
Expand Down