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fix custom kernel registration (#12674)

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Yishuo Wang 2025-01-08 17:39:17 +08:00 committed by GitHub
parent a22a8c21bb
commit 5c24276fc4
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1 changed files with 25 additions and 19 deletions
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58
python/llm/src/ipex_llm/transformers/xpu_ops.py
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@ -20,9 +20,9 @@ import xe_batch
import xe_addons import xe_addons
@torch.library.register_fake("ipex_llm::forward_new") # @torch.library.register_fake("ipex_llm::forward_new")
def _(x, weight, qtype, input_size): # def _(x, weight, qtype, input_size):
return torch.empty_like(x) # return ???
# @torch.library.register_fake("ipex_llm::dequant") # @torch.library.register_fake("ipex_llm::dequant")
@ -32,34 +32,40 @@ def _(x, weight, qtype, input_size):
@torch.library.register_fake("ipex_llm::mlp_forward_xpu") @torch.library.register_fake("ipex_llm::mlp_forward_xpu")
def _(x, weight1, weight2, batch_size, state_size, output_size, act_type, qtype): def _(x, weight1, weight2, batch_size, state_size, output_size, act_type, qtype):
return torch.empty_like(x) return torch.empty([batch_size, output_size],
dtype=x.dtype, device=x.device)
# @torch.library.register_fake("ipex_llm::rwkv_linear_attention_v4") @torch.library.register_fake("ipex_llm::rwkv_linear_attention_v4")
# def _(time_decay, time_first, key, value, num_state, den_state, max_state) def _(time_decay, time_first, key, value, num_state, den_state, max_state):
return torch.empty_like(key)
@torch.library.register_fake("ipex_llm::rwkv_linear_attention_v5")
def _(time_decay, time_first, receptance, key, value, state):
bsz, n_heads, seq_len, head_dim = key.shape
return torch.empty([bsz, seq_len, n_heads, head_dim],
dtype=key.dtype, device=key.device)
@torch.library.register_fake("ipex_llm::rwkv_time_shift")
def _(hidden, shifted, mix):
bsz, seq_len, hidden_size = hidden.shape
return torch.empty([mix.size(0), bsz, seq_len, hidden_size],
dtype=hidden.dtype, device=hidden.device)
@torch.library.register_fake("ipex_llm::dequantize_rows")
def _(x, weight, qtype, state_size, output_size):
return torch.empty([x.size(0), x.size(1), state_size],
dtype=torch.float, device=weight.device)
# @torch.library.register_fake("ipex_llm::batch_forward")
# def _(x, weight, qtype):
# return ??? # return ???
# @torch.library.register_fake("ipex_llm::rwkv_linear_attention_v5")
# def _(time_decay, time_first, receptance, key, value, state)
# return ???
# @torch.library.register_fake("ipex_llm::rwkv_time_shift")
# def _(hidden, shifted, mix):
# return ???
# @torch.library.register_fake("ipex_llm::dequantize_rows")
# def _(x, weight, qtype, state_size, output_size):
# return ???
@torch.library.register_fake("ipex_llm::batch_forward")
def _(x, weight, qtype):
return torch.empty_like(x)
@torch.library.register_fake("ipex_llm::sdp") @torch.library.register_fake("ipex_llm::sdp")
def _(query, key, value, mask): def _(query, key, value, mask):
return torch.empty(query.shape, dtype=query.dtype, device=query.device) return torch.empty(query.shape, dtype=query.dtype, device=query.device)