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refactor: Remove head_dim padding logic in weight_loader #411

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refactor: Remove head_dim padding logic in weight_loader #411

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143 changes: 3 additions & 140 deletions python/sgl_jax/srt/utils/weight_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -78,7 +78,6 @@ def __init__(
)

self.head_dim = (self.head_dim_original + 127) // 128 * 128
self.head_dim_pad = self.head_dim - self.head_dim_original

if hasattr(self.mesh, "shape") and "tensor" in self.mesh.shape:
self.sharding_size = self.mesh.shape["tensor"]
Expand Down Expand Up @@ -281,9 +280,6 @@ def _handle_single_weight(
if mapping.reshape is not None:
processed_weight = jnp.reshape(processed_weight, mapping.reshape)

if mapping.head_dim_padding and self.head_dim_pad > 0:
processed_weight = self._apply_head_dim_padding(processed_weight, hf_key, mapping)

if mapping.kv_head_padding:
processed_weight = self._apply_kv_head_padding(processed_weight, hf_key)

Expand Down Expand Up @@ -313,32 +309,16 @@ def _split_qkv_weight(
):
jax_paths = mapping.target_path

if hf_key.endswith(".bias"):
q_dim = self.num_heads * self.head_dim_original
kv_dim = self.num_kv_heads * self.head_dim_original
q_dim = self.num_heads * self.head_dim_original
kv_dim = self.num_kv_heads * self.head_dim_original

if hf_key.endswith(".bias"):
q_bias = weight[:q_dim]
k_bias = weight[q_dim : q_dim + kv_dim]
v_bias = weight[q_dim + kv_dim : q_dim + 2 * kv_dim]

if mapping.head_dim_padding and self.head_dim_pad > 0:
q_bias = jnp.reshape(q_bias, (self.num_heads, self.head_dim_original))
q_bias = jnp.pad(q_bias, ((0, 0), (0, self.head_dim_pad)))
q_bias = jnp.reshape(q_bias, (self.num_heads * self.head_dim,))

k_bias = jnp.reshape(k_bias, (self.num_kv_heads, self.head_dim_original))
k_bias = jnp.pad(k_bias, ((0, 0), (0, self.head_dim_pad)))
k_bias = jnp.reshape(k_bias, (self.num_kv_heads * self.head_dim,))

v_bias = jnp.reshape(v_bias, (self.num_kv_heads, self.head_dim_original))
v_bias = jnp.pad(v_bias, ((0, 0), (0, self.head_dim_pad)))
v_bias = jnp.reshape(v_bias, (self.num_kv_heads * self.head_dim,))

splits = [q_bias, k_bias, v_bias]
else:
q_dim = self.num_heads * self.head_dim_original
kv_dim = self.num_kv_heads * self.head_dim_original

if mapping.transpose:
q_weight = weight[:, :q_dim]
k_weight = weight[:, q_dim : q_dim + kv_dim]
Expand All @@ -348,62 +328,6 @@ def _split_qkv_weight(
k_weight = weight[q_dim : q_dim + kv_dim, :]
v_weight = weight[q_dim + kv_dim : q_dim + 2 * kv_dim, :]

if mapping.head_dim_padding and self.head_dim_pad > 0:
if mapping.transpose:
q_weight = jnp.reshape(
q_weight,
(self.hidden_size, self.num_heads, self.head_dim_original),
)
q_weight = jnp.pad(q_weight, ((0, 0), (0, 0), (0, self.head_dim_pad)))
q_weight = jnp.reshape(
q_weight, (self.hidden_size, self.num_heads * self.head_dim)
)

k_weight = jnp.reshape(
k_weight,
(self.hidden_size, self.num_kv_heads, self.head_dim_original),
)
k_weight = jnp.pad(k_weight, ((0, 0), (0, 0), (0, self.head_dim_pad)))
k_weight = jnp.reshape(
k_weight, (self.hidden_size, self.num_kv_heads * self.head_dim)
)

v_weight = jnp.reshape(
v_weight,
(self.hidden_size, self.num_kv_heads, self.head_dim_original),
)
v_weight = jnp.pad(v_weight, ((0, 0), (0, 0), (0, self.head_dim_pad)))
v_weight = jnp.reshape(
v_weight, (self.hidden_size, self.num_kv_heads * self.head_dim)
)
else:
q_weight = jnp.reshape(
q_weight,
(self.num_heads, self.head_dim_original, self.hidden_size),
)
q_weight = jnp.pad(q_weight, ((0, 0), (0, self.head_dim_pad), (0, 0)))
q_weight = jnp.reshape(
q_weight, (self.num_heads * self.head_dim, self.hidden_size)
)

k_weight = jnp.reshape(
k_weight,
(self.num_kv_heads, self.head_dim_original, self.hidden_size),
)
k_weight = jnp.pad(k_weight, ((0, 0), (0, self.head_dim_pad), (0, 0)))
k_weight = jnp.reshape(
k_weight, (self.num_kv_heads * self.head_dim, self.hidden_size)
)

v_weight = jnp.reshape(
v_weight,
(self.num_kv_heads, self.head_dim_original, self.hidden_size),
)
v_weight = jnp.pad(v_weight, ((0, 0), (0, self.head_dim_pad), (0, 0)))
v_weight = jnp.reshape(
v_weight, (self.num_kv_heads * self.head_dim, self.hidden_size)
)

splits = [q_weight, k_weight, v_weight]

for split_weight, jax_path in zip(splits, jax_paths):
Expand Down Expand Up @@ -460,67 +384,6 @@ def _get_param(self, params: nnx.State, path: str) -> nnx.State:

return current_level

def _apply_head_dim_padding(
self, weight: jax.Array, hf_key: str, mapping: WeightMapping
) -> jax.Array:
if hf_key.endswith(".bias"):
if any(proj in hf_key for proj in ["q_proj", "k_proj", "v_proj"]):
if "q_proj" in hf_key:
reshaped = jnp.reshape(weight, (self.num_heads, self.head_dim_original))
padded = jnp.pad(reshaped, ((0, 0), (0, self.head_dim_pad)))
return jnp.reshape(padded, (self.num_heads * self.head_dim,))
else: # k_proj or v_proj
reshaped = jnp.reshape(weight, (self.num_kv_heads, self.head_dim_original))
padded = jnp.pad(reshaped, ((0, 0), (0, self.head_dim_pad)))
return jnp.reshape(padded, (self.num_kv_heads * self.head_dim,))
else:
if mapping.reshape is not None:
if "o_proj" in hf_key:
padded = jnp.pad(weight, ((0, 0), (0, 0), (0, self.head_dim_pad)))
else:
padded = jnp.pad(weight, ((0, 0), (0, self.head_dim_pad), (0, 0)))
return padded
else:
if mapping.transpose:
if "q_proj" in hf_key:
reshaped = jnp.reshape(
weight,
(self.hidden_size, self.num_heads, self.head_dim_original),
)
padded = jnp.pad(reshaped, ((0, 0), (0, 0), (0, self.head_dim_pad)))
return jnp.reshape(
padded, (self.hidden_size, self.num_heads * self.head_dim)
)
elif any(proj in hf_key for proj in ["k_proj", "v_proj"]):
reshaped = jnp.reshape(
weight,
(
self.hidden_size,
self.num_kv_heads,
self.head_dim_original,
),
)
padded = jnp.pad(reshaped, ((0, 0), (0, 0), (0, self.head_dim_pad)))
return jnp.reshape(
padded,
(self.hidden_size, self.num_kv_heads * self.head_dim),
)
elif "o_proj" in hf_key:
reshaped = jnp.reshape(
weight,
(self.num_heads * self.head_dim_original, self.hidden_size),
)
padded_reshaped = jnp.reshape(
reshaped,
(self.num_heads, self.head_dim_original, self.hidden_size),
)
padded = jnp.pad(padded_reshaped, ((0, 0), (0, self.head_dim_pad), (0, 0)))
return jnp.reshape(
padded, (self.num_heads * self.head_dim, self.hidden_size)
)

return weight

def _apply_kv_head_padding(self, weight: jax.Array, hf_key: str) -> jax.Array:
"""Apply KV head padding/replication when tp_size > total_kv_heads."""
if any(
Expand Down