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[Fix] Fix the two-stage softmax func by removing log2e #2269

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merged 2 commits into from
May 3, 2024
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[Fix] Fix the two-stage softmax func by removing log2e #2269

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92ca122
[Fix] Fix the two-stage softmax func by removing log2e
MasterJH5574 May 3, 2024
da1af23
Remove redundant import
MasterJH5574 May 3, 2024
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16 changes: 6 additions & 10 deletions python/mlc_llm/compiler_pass/rewrite_softmax.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,5 @@
"""A compiler pass that rewrites one-shot softmax into two-stage softmax."""

import math

import tvm
from tvm import relax
from tvm.ir.module import IRModule
Expand Down Expand Up @@ -81,8 +79,6 @@ def visit_call_(self, call: relax.Call) -> Expr: # pylint: disable=arguments-re
def _get_lse_and_softmax_func( # pylint: disable=too-many-locals,too-many-statements
target: tvm.target.Target, chunk_size: int
):
log2e = math.log2(math.exp(1))

# pylint: disable=invalid-name
@T.prim_func
def chunk_lse(var_A: T.handle, var_chunked_lse: T.handle): # pylint: disable=too-many-locals
Expand Down Expand Up @@ -117,13 +113,13 @@ def chunk_lse(var_A: T.handle, var_chunked_lse: T.handle): # pylint: disable=to
temp_sum[v0, v1] = T.float32(0)
temp_sum[v0, v1] += T.if_then_else(
v1 * T.int64(chunk_size) + v2 < vocab_size,
T.exp2((A_pad[v0, v1, v2] - temp_max[v0, v1]) * log2e),
T.exp(A_pad[v0, v1, v2] - temp_max[v0, v1]),
T.float32(0),
)
for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(1)):
with T.block("log"):
v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2])
chunked_lse[v0, v1] = T.log2(temp_sum[v0, v1]) + temp_max[v0, v1] * log2e
chunked_lse[v0, v1] = T.log(temp_sum[v0, v1]) + temp_max[v0, v1]

@T.prim_func
def softmax_with_chunked_lse(var_A: T.handle, var_chunked_lse: T.handle, var_softmax: T.handle):
Expand All @@ -148,17 +144,17 @@ def softmax_with_chunked_lse(var_A: T.handle, var_chunked_lse: T.handle, var_sof
v0, v1 = T.axis.remap("SR", [l0, l1])
with T.init():
temp_sum[v0] = T.float32(0)
temp_sum[v0] += T.exp2(chunked_lse[v0, v1] - temp_max[v0])
temp_sum[v0] += T.exp(chunked_lse[v0, v1] - temp_max[v0])
for l0 in T.serial(0, batch_size):
with T.block("log"):
v0 = T.axis.remap("S", [l0])
lse[v0] = T.log2(temp_sum[v0]) + temp_max[v0]
lse[v0] = T.log(temp_sum[v0]) + temp_max[v0]
for l0, l1, l2 in T.grid(batch_size, num_chunks, T.int64(chunk_size)):
with T.block("pad"):
v0, v1, v2 = T.axis.remap("SSS", [l0, l1, l2])
if v1 * T.int64(chunk_size) + v2 < vocab_size:
softmax[v0, v1 * T.int64(chunk_size) + v2] = T.exp2(
A[v0, v1 * T.int64(chunk_size) + v2] * log2e - lse[v0]
softmax[v0, v1 * T.int64(chunk_size) + v2] = T.exp(
A[v0, v1 * T.int64(chunk_size) + v2] - lse[v0]
)

sch = tvm.tir.Schedule(IRModule({"softmax_with_chunked_lse": softmax_with_chunked_lse}))
Expand Down