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[Relax] Fix llama4_rope_with_position_map to support partial rotary factor #18520

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[Relax] Fix llama4_rope_with_position_map to support partial rotary factor #18520

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129 changes: 93 additions & 36 deletions python/tvm/relax/frontend/nn/llm/position_embedding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,7 +117,11 @@ def rope_freq_llama4( # pylint: disable=too-many-arguments,too-many-locals
smoothed_freq_var = tir.Var("smoothed_freq", "float32")
cos_freq = tir.cos(smoothed_freq_var).astype(dtype)
sin_freq = tir.sin(smoothed_freq_var).astype(dtype)
return cos_freq, sin_freq, {smoothed_freq_var: smoothed_freq, orig_freq_var: orig_freq}
return (
cos_freq,
sin_freq,
{smoothed_freq_var: smoothed_freq, orig_freq_var: orig_freq},
)


def rope_freq_llama3( # pylint: disable=too-many-arguments,too-many-locals
Expand Down Expand Up @@ -147,7 +151,11 @@ def rope_freq_llama3( # pylint: disable=too-many-arguments,too-many-locals
smoothed_freq_var = tir.Var("smoothed_freq", "float32")
cos_freq = tir.cos(smoothed_freq_var).astype(dtype)
sin_freq = tir.sin(smoothed_freq_var).astype(dtype)
return cos_freq, sin_freq, {smoothed_freq_var: smoothed_freq, orig_freq_var: orig_freq}
return (
cos_freq,
sin_freq,
{smoothed_freq_var: smoothed_freq, orig_freq_var: orig_freq},
)


def rope_freq_longrope( # pylint: disable=too-many-arguments
Expand Down Expand Up @@ -275,7 +283,7 @@ def switch_rope_freq_func(rope_scaling: Dict[str, Any]) -> Callable:
beta_fast=rope_scaling["beta_fast"],
beta_slow=rope_scaling["beta_slow"],
)
raise ValueError(f'Unsupported RoPE scaling type: {rope_scaling["rope_type"]}')
raise ValueError(f"Unsupported RoPE scaling type: {rope_scaling['rope_type']}")


# mypy: disable-error-code="attr-defined"
Expand Down Expand Up @@ -570,7 +578,10 @@ def fused_rope_longrope_scaling( # pylint: disable=too-many-locals
# long factors is the first half, short factors is the second half
long_factors = T.Buffer((rotary_dim // 2,), "float32", data=ext_factors.data)
short_factors = T.Buffer(
(rotary_dim // 2,), "float32", data=ext_factors.data, elem_offset=(rotary_dim // 2)
(rotary_dim // 2,),
"float32",
data=ext_factors.data,
elem_offset=(rotary_dim // 2),
)

if seq_len > original_max_position_embeddings:
Expand Down Expand Up @@ -687,6 +698,10 @@ def llama4_rope_with_position_map( # pylint: disable=too-many-arguments
rotary_dim = head_dim
scale = tir.const(scale, "float32")
is_longrope_scaling = rope_scaling.get("rope_type") == "longrope"
if is_longrope_scaling and "original_max_position_embeddings" in rope_scaling:
original_max_position_embeddings = rope_scaling["original_max_position_embeddings"]
else:
original_max_position_embeddings = 0

def _rope( # pylint: disable=too-many-arguments
x: T.Buffer,
Expand Down Expand Up @@ -770,7 +785,7 @@ def fused_rope_longrope_scaling( # pylint: disable=too-many-locals
var_q: T.handle,
var_k: T.handle,
var_v: T.handle,
ext_factors: T.Buffer((rotary_dim // 2,), "float32"), # type: ignore
ext_factors: T.Buffer((rotary_dim,), "float32"), # type: ignore
):
T.func_attr(
{
Expand All @@ -787,37 +802,79 @@ def fused_rope_longrope_scaling( # pylint: disable=too-many-locals
position_map = T.match_buffer(
var_position_map, (seq_len,), "int32", elem_offset=position_map_elem_offset
)
for iters in T.grid(seq_len, fused_heads, head_dim):
with T.block("llama_fused_rope"):
s, h, d = T.axis.remap("SSS", iters)
if h < num_q_heads:
q[s, h, d] = T.if_then_else(
d < rotary_dim,
_rope(
qkv,
s,
h,
d,
position_map[s],
ext_factors if is_longrope_scaling else None,
),
qkv[s, h, d],
)
elif h < num_q_heads + num_kv_heads:
k[s, h - num_q_heads, d] = T.if_then_else(
d < rotary_dim,
_rope(
qkv,
s,
h,
d,
position_map[s],
ext_factors if is_longrope_scaling else None,
),
qkv[s, h, d],
)
else:
v[s, h - (num_q_heads + num_kv_heads), d] = qkv[s, h, d]
# long factors is the first half, short factors is the second half
long_factors = T.Buffer((rotary_dim // 2,), "float32", data=ext_factors.data)
short_factors = T.Buffer(
(rotary_dim // 2,),
"float32",
data=ext_factors.data,
elem_offset=(rotary_dim // 2),
)

if seq_len > original_max_position_embeddings:
for iters in T.grid(seq_len, fused_heads, head_dim):
with T.block("llama_fused_rope"):
s, h, d = T.axis.remap("SSS", iters)
if h < num_q_heads:
q[s, h, d] = T.if_then_else(
d < rotary_dim,
_rope(
qkv,
s,
h,
d,
position_map[s],
long_factors if is_longrope_scaling else None,
),
qkv[s, h, d],
)
elif h < num_q_heads + num_kv_heads:
k[s, h - num_q_heads, d] = T.if_then_else(
d < rotary_dim,
_rope(
qkv,
s,
h,
d,
position_map[s],
long_factors if is_longrope_scaling else None,
),
qkv[s, h, d],
)
else:
v[s, h - (num_q_heads + num_kv_heads), d] = qkv[s, h, d]
else:
for iters in T.grid(seq_len, fused_heads, head_dim):
with T.block("llama_fused_rope"):
s, h, d = T.axis.remap("SSS", iters)
if h < num_q_heads:
q[s, h, d] = T.if_then_else(
d < rotary_dim,
_rope(
qkv,
s,
h,
d,
position_map[s],
short_factors if is_longrope_scaling else None,
),
qkv[s, h, d],
)
elif h < num_q_heads + num_kv_heads:
k[s, h - num_q_heads, d] = T.if_then_else(
d < rotary_dim,
_rope(
qkv,
s,
h,
d,
position_map[s],
short_factors if is_longrope_scaling else None,
),
qkv[s, h, d],
)
else:
v[s, h - (num_q_heads + num_kv_heads), d] = qkv[s, h, d]

if is_longrope_scaling:
return fused_rope_longrope_scaling
Expand Down
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