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Update cross_entropy_loss.py

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Daniel Han 2024-10-31 01:56:03 -07:00
commit dd5d035a99
1 changed files with 32 additions and 32 deletions
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unsloth/kernels/cross_entropy_loss.py
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@ -19,23 +19,23 @@ from .utils import calculate_settings, MAX_FUSED_SIZE, triton_tanh
from transformers.models.llama.modeling_llama import logger
from packaging.version import Version
# @triton.heuristics({
# "DO_SOFTCAPPING": lambda args: args["DO_SOFTCAPPING" ],
# "DO_LOGIT_SCALING": lambda args: args["DO_LOGIT_SCALING"],
# })
@triton.heuristics({
"DO_SOFTCAPPING": lambda args: args["DO_SOFTCAPPING" ],
"DO_LOGIT_SCALING": lambda args: args["DO_LOGIT_SCALING"],
})
@triton.jit
def _cross_entropy_forward(
logits_ptr ,
logits_row_stride ,
logits_row_stride : tl.constexpr(tl.int64),
loss_ptr ,
logsumexp_ptr ,
labels_ptr ,
VOCAB_SIZE ,
VOCAB_SIZE : tl.constexpr,
BLOCK_SIZE : tl.constexpr,
DO_SOFTCAPPING ,
SOFTCAP ,
DO_LOGIT_SCALING ,
LOGIT_SCALE ,
DO_SOFTCAPPING : tl.constexpr(tl.int1),
SOFTCAP : tl.constexpr(tl.float32),
DO_LOGIT_SCALING : tl.constexpr(tl.int1),
LOGIT_SCALE : tl.constexpr(tl.float32),
):
"""
Cross Entropy Loss = 1/n sum [ -yi log(Pi) ]
@ -67,14 +67,14 @@ def _cross_entropy_forward(
mask = col_offsets < VOCAB_SIZE
label_idx = tl.load(labels_ptr).to(tl.int32)
logits = tl.load(logits_ptr + col_offsets, mask = mask, other = -float("inf")).to(tl.float32)
logits = tl.load(logits_ptr + col_offsets, mask = mask, other = -float("inf"))
# Go logit scaling for Cohere: t * x
if DO_LOGIT_SCALING: logits = LOGIT_SCALE * logits
# Do logit softcapping for Gemma 2: t * tanh(1/t * x)
if DO_SOFTCAPPING: logits = SOFTCAP * triton_tanh(logits / SOFTCAP)
if DO_SOFTCAPPING: logits = SOFTCAP * triton_tanh(logits.to(tl.float32) / SOFTCAP).to(logits.dtype)
# logits = logits
logits = logits.to(tl.float32)
c = tl.max(logits, 0)
logsumexp = c + tl.log(tl.sum(tl.exp(logits - c), 0))
@ -99,7 +99,7 @@ pass
@triton.jit
def _chunked_cross_entropy_forward(
logits_ptr ,
logits_row_stride ,
logits_row_stride : tl.constexpr(tl.int64),
loss_ptr ,
logsumexp_ptr ,
labels_ptr ,
@ -107,9 +107,9 @@ def _chunked_cross_entropy_forward(
N_CHUNKS ,
BLOCK_SIZE : tl.constexpr,
DO_SOFTCAPPING : tl.constexpr(tl.int1),
SOFTCAP ,
SOFTCAP : tl.constexpr(tl.float32),
DO_LOGIT_SCALING : tl.constexpr(tl.int1),
LOGIT_SCALE ,
LOGIT_SCALE : tl.constexpr(tl.float32),
):
"""
256K vocab divided in 4 chunks
@ -146,14 +146,14 @@ def _chunked_cross_entropy_forward(
mask = col_offsets < VOCAB_SIZE
label_idx = tl.load(labels_ptr).to(tl.int32)
logits = tl.load(logits_ptr + col_offsets, mask = mask, other = -float("inf")).to(tl.float32)
logits = tl.load(logits_ptr + col_offsets, mask = mask, other = -float("inf"))
# Go logit scaling for Cohere: t * x
if DO_LOGIT_SCALING: logits = LOGIT_SCALE * logits
# Do logit softcapping for Gemma 2: t * tanh(1/t * x)
if DO_SOFTCAPPING: logits = SOFTCAP * triton_tanh(logits / SOFTCAP)
if DO_SOFTCAPPING: logits = SOFTCAP * triton_tanh(logits.to(tl.float32) / SOFTCAP).to(logits.dtype)
# logits = logits.to(tl.float32)
logits = logits.to(tl.float32)
c = tl.max(logits, 0)
logsumexp = c + tl.log(tl.sum(tl.exp(logits - c), 0))
@ -175,14 +175,14 @@ def _chunked_cross_entropy_forward(
pass
# @triton.heuristics({
# "DO_SOFTCAPPING": lambda args: args["DO_SOFTCAPPING" ],
# "DO_LOGIT_SCALING": lambda args: args["DO_LOGIT_SCALING"],
# })
@triton.heuristics({
"DO_SOFTCAPPING": lambda args: args["DO_SOFTCAPPING" ],
"DO_LOGIT_SCALING": lambda args: args["DO_LOGIT_SCALING"],
})
@triton.jit
def _cross_entropy_backward(
logits_ptr ,
logits_row_stride ,
logits_row_stride : tl.constexpr(tl.int64),
dloss_ptr ,
dloss_row_stride ,
logsumexp_ptr ,
@ -190,9 +190,9 @@ def _cross_entropy_backward(
VOCAB_SIZE ,
BLOCK_SIZE : tl.constexpr,
DO_SOFTCAPPING : tl.constexpr(tl.int1),
SOFTCAP ,
SOFTCAP : tl.constexpr(tl.float32),
DO_LOGIT_SCALING : tl.constexpr(tl.int1),
LOGIT_SCALE ,
LOGIT_SCALE : tl.constexpr(tl.float32),
):
"""
CE_i = -y log(P) = y * (log[sum(exp(x))] - x)
@ -223,7 +223,7 @@ def _cross_entropy_backward(
else:
dloss = 0.0
x = tl.load(logits_ptr + col_offsets, mask = mask, other = -float("inf")).to(tl.float32)
x = tl.load(logits_ptr + col_offsets, mask = mask, other = -float("inf"))
# Do logit scaling for Cohere
if DO_LOGIT_SCALING:
@ -235,12 +235,12 @@ def _cross_entropy_backward(
partial = x
if DO_SOFTCAPPING:
# d/dx [t * tanh(1/t * x)] = 1 - tanh^2(1/t * x)
partial = triton_tanh(x / SOFTCAP)
partial = triton_tanh(x.to(tl.float32) / SOFTCAP).to(x.dtype)
x = SOFTCAP * partial
pass
logsumexp = tl.load(logsumexp_ptr + row_idx)
y = tl.exp(x - logsumexp)
y = tl.exp(x.to(tl.float32) - logsumexp)
y = tl.where(
col_offsets == label_idx,
y - 1.0, # exp(x - logsumexp) - 1
@ -271,7 +271,7 @@ class Fast_CrossEntropyLoss(torch.autograd.Function):
div, mod = divmod(vocab_size, MAX_FUSED_SIZE)
n_chunks = div + (mod != 0)
losses = torch.empty(n_rows, dtype = torch.float32, device = logits.device)
losses = torch.empty(n_rows, dtype = torch.float32, device = "cuda:0")
DO_SOFTCAPPING : bool = bool(logit_softcapping != 0)
DO_LOGIT_SCALING : bool = bool(logit_scaling != 0)
@ -279,7 +279,7 @@ class Fast_CrossEntropyLoss(torch.autograd.Function):
if n_chunks == 1:
# For small vocabs <= 65336 like Llama, Mistral
BLOCK_SIZE, num_warps = calculate_settings(vocab_size)
logsumexp = torch.empty(n_rows, dtype = torch.float32, device = logits.device)
logsumexp = torch.empty(n_rows, dtype = torch.float32, device = "cuda:0")
_cross_entropy_forward[(n_rows,)](
logits, logits.stride(0),
@ -296,7 +296,7 @@ class Fast_CrossEntropyLoss(torch.autograd.Function):
)
else:
# For large vocabs > 65336 like Gemma 256K
logsumexp = torch.empty((n_rows, n_chunks,), dtype = torch.float32, device = logits.device)
logsumexp = torch.empty((n_rows, n_chunks,), dtype = torch.float32, device = "cuda:0")
_chunked_cross_entropy_forward[(n_rows, n_chunks,)](
logits, logits.stride(0),