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perf: add preshuffle logic for HIP fused_moe #1057

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perf: add preshuffle logic for HIP fused_moe #1057

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70 changes: 48 additions & 22 deletions atom/model_ops/moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -920,34 +920,60 @@ def process_weights_after_loading(self, layer):
layer.w2_weight_scale = None
return

# shuffle weight
E13, N13, K13 = layer.w13_weight_scale.data.shape
layer.w13_weight.data = shuffle_weight(
layer.w13_weight,
is_guinterleave=self.is_guinterleave,
gate_up=True,
layer.w13_weight.data, layout=(16, 16), is_guinterleave=True, gate_up=True
)
Comment on lines +923 to 926
layer.w2_weight.data = shuffle_weight(
layer.w2_weight,
is_guinterleave=self.is_guinterleave,
gate_up=False,
)
layer.w13_weight.is_shuffled = True
layer.w2_weight.is_shuffled = True

# shuffle scale
w13_scale_2d = layer.w13_weight_scale.reshape(
-1, layer.w13_weight_scale.shape[-1]
layer.w13_weight_scale = atom_parameter(
shuffle_scale(
layer.w13_weight_scale.data.reshape(E13 * N13, K13),
experts_cnt=E13, is_guinterleave=True, gate_up=True,
).reshape(E13, N13, K13)
)
w2_scale_2d = layer.w2_weight_scale.reshape(-1, layer.w2_weight_scale.shape[-1])

shuffled_w13_scale = shuffle_scale(
w13_scale_2d, self.num_experts, self.is_guinterleave, True
E2, N2, K2 = layer.w2_weight_scale.data.shape
layer.w2_weight.data = shuffle_weight(
layer.w2_weight.data, layout=(16, 16), is_guinterleave=True, gate_up=False
)
Comment on lines +934 to 937
shuffled_w2_scale = shuffle_scale(
w2_scale_2d, self.num_experts, self.is_guinterleave, False
layer.w2_weight_scale = atom_parameter(
shuffle_scale(
layer.w2_weight_scale.data.reshape(E2 * N2, K2),
experts_cnt=E2, is_guinterleave=True, gate_up=False,
).reshape(E2, N2, K2)
)
Comment on lines +923 to 943
layer.w13_weight_scale = atom_parameter(shuffled_w13_scale)
layer.w2_weight_scale = atom_parameter(shuffled_w2_scale)

layer.w13_weight.is_shuffled = True
layer.w2_weight.is_shuffled = True
layer.w13_weight.shuffle_kind = layer.w13_weight_scale.shuffle_kind = "mxfp4_moe"
layer.w2_weight.shuffle_kind = layer.w2_weight_scale.shuffle_kind = "mxfp4_moe"

# layer.w13_weight.data = shuffle_weight(
# layer.w13_weight,
# is_guinterleave=self.is_guinterleave,
# gate_up=True,
# )
# layer.w2_weight.data = shuffle_weight(
# layer.w2_weight,
# is_guinterleave=self.is_guinterleave,
# gate_up=False,
# )
# layer.w13_weight.is_shuffled = True
# layer.w2_weight.is_shuffled = True

# # shuffle scale
# w13_scale_2d = layer.w13_weight_scale.reshape(
# -1, layer.w13_weight_scale.shape[-1]
# )
# w2_scale_2d = layer.w2_weight_scale.reshape(-1, layer.w2_weight_scale.shape[-1])

# shuffled_w13_scale = shuffle_scale(
# w13_scale_2d, self.num_experts, self.is_guinterleave, True
# )
# shuffled_w2_scale = shuffle_scale(
# w2_scale_2d, self.num_experts, self.is_guinterleave, False
# )
# layer.w13_weight_scale = atom_parameter(shuffled_w13_scale)
# layer.w2_weight_scale = atom_parameter(shuffled_w2_scale)

def get_fused_moe_quant_config(
self, layer: torch.nn.Module
Expand Down