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import torch
import torch_xla
import torch_xla.distributed.spmd as xs
import torch_xla.core.xla_model as xm
import pickle
import jax
import os
from torch_xla.experimental.custom_kernel import (
FlashAttention,
jax_import_guard,
trace_pallas,
)
def flash_attention(
q, # [batch_size, num_heads, q_seq_len, d_model]
k, # [batch_size, num_heads, kv_seq_len, d_model]
v, # [batch_size, num_heads, kv_seq_len, d_model]
causal=False,
q_segment_ids=None, # [batch_size, q_seq_len]
kv_segment_ids=None, # [batch_size, kv_seq_len]
sm_scale=1.0,
*,
ab=None, # [batch_size, num_heads, q_seq_len, kv_seq_len]
partition_spec=None,
mesh=None,
):
# TODO: support SPMD and Dynamo with segment_ids.
return SPMDFlashAttention.apply(
q,
k,
v,
causal,
q_segment_ids,
kv_segment_ids,
sm_scale,
ab,
partition_spec,
mesh,
)
class SPMDFlashAttention(FlashAttention):
"""
This is a simplified wrapper on top of https://github.com/google/jax/blob/b2058d72b7e1693a41303d5411572aabf99b7981/jax/experimental/pallas/ops/tpu/flash_attention.py#L139
where we only takes q, k, v and causal as input and set block_sizes for the users.
"""
@staticmethod
def forward(
ctx,
q,
k,
v,
causal,
q_segment_ids,
kv_segment_ids,
sm_scale,
ab,
partition_spec,
mesh,
):
# Import JAX within the function such that we don't need to call the jax_import_guard()
# in the global scope which could cause problems for xmp.spawn.
jax_import_guard()
import jax # noqa: F401
from jax.experimental.pallas.ops.tpu.flash_attention import (
_flash_attention_impl,
)
ctx.causal = causal
ctx.sm_scale = sm_scale
ctx.partition_spec = partition_spec
ctx.mesh = mesh
ctx.q_full_shape = None
ctx.kv_full_shape = None
save_residuals = q.requires_grad or k.requires_grad or v.requires_grad
# SPMD integration.
# mark_sharding is in-placed, and therefore save the full q, k, v for the backward.
full_q = q
full_k = k
full_v = v
full_ab = ab
if partition_spec is not None:
ctx.q_full_shape = q.shape
ctx.kv_full_shape = k.shape
q = xs.enable_manual_sharding(q, partition_spec, mesh=mesh).global_tensor
k = xs.enable_manual_sharding(k, partition_spec, mesh=mesh).global_tensor
v = xs.enable_manual_sharding(v, partition_spec, mesh=mesh).global_tensor
if ab:
ab = xs.enable_manual_sharding(
ab, partition_spec, mesh=mesh
).global_tensor
# It computes the shape and type of o, l, m.
shapes = [q.shape]
dtypes = [q.dtype]
if save_residuals:
res_shape = list(q.shape)
res_shape[-1] = FlashAttention.MIN_BLOCK_SIZE
for _ in range(2):
shapes.append(res_shape)
dtypes.append(torch.float32)
with torch.no_grad():
if (
partition_spec is not None
and q_segment_ids is not None
and kv_segment_ids is not None
):
# partition_spec is for q,k,v with shape [batch, num_head, seq_len, head_dim], segment id
# is of shape [batch, seq_len], hence we need to tweak it a bit
segment_id_partition_spec = (partition_spec[0], partition_spec[2])
q_segment_ids = xs.enable_manual_sharding(
q_segment_ids, (partition_spec[0], partition_spec[2]), mesh=mesh
).global_tensor
kv_segment_ids = xs.enable_manual_sharding(
kv_segment_ids, segment_id_partition_spec, mesh=mesh
).global_tensor
segment_ids, q_segment_ids_fa, kv_segment_ids_fa = (
FlashAttention.prepare_segment_ids(q_segment_ids, kv_segment_ids)
)
ctx.segment_ids = segment_ids
# We can't directly use flash_attention as we need to override the save_residuals flag which returns
# l and m that is needed for the backward. Then we lose all the shape checks.
# TODO: replicate the shape checks on flash_attention.
# Here we seperate the tracing and execution part just to support SegmentIds.
payload, _ = trace_pallas(
_flash_attention_impl,
q,
k,
v,
ab,
segment_ids,
save_residuals,
causal,
sm_scale,
min(FlashAttention.DEFAULT_BLOCK_SIZES["block_b"], q.shape[0]),
min(FlashAttention.DEFAULT_BLOCK_SIZES["block_q"], q.shape[2]),
min(FlashAttention.DEFAULT_BLOCK_SIZES["block_k_major"], k.shape[2]),
min(FlashAttention.DEFAULT_BLOCK_SIZES["block_k"], k.shape[2]),
False,
static_argnums=range(5, 13),
use_cache=True,
)
args = [q, k, v]
if ab is not None:
args += [ab]
if segment_ids is not None:
args += [q_segment_ids_fa, kv_segment_ids_fa]
o = torch_xla._XLAC._xla_tpu_custom_call(args, payload, shapes, dtypes)
if not save_residuals:
o = o[0]
# SPMD integration
if partition_spec is not None:
o = xs.disable_manual_sharding(
o, partition_spec, ctx.q_full_shape, mesh=mesh
).global_tensor
return o
o, *aux = o
l, m = (v[..., 0] for v in aux[-2:]) # noqa: E741
# SPMD integration
if partition_spec is not None:
o = xs.disable_manual_sharding(
o, partition_spec, ctx.q_full_shape, mesh=mesh
).global_tensor
l = xs.disable_manual_sharding( # noqa: E741
l, partition_spec[0:3], ctx.q_full_shape[0:3], mesh=mesh
).global_tensor
m = xs.disable_manual_sharding(
m, partition_spec[0:3], ctx.q_full_shape[0:3], mesh=mesh
).global_tensor
ctx.save_for_backward(
full_q,
full_k,
full_v,
o,
l,
m,
q_segment_ids_fa,
kv_segment_ids_fa,
full_ab,
)
return o
@staticmethod
def backward(ctx, grad_output):
from jax.experimental.pallas.ops.tpu.flash_attention import (
_flash_attention_bwd_dq,
_flash_attention_bwd_dkv,
)
q, k, v, o, l, m, q_segment_ids_fa, kv_segment_ids_fa, ab = ( # noqa: E741
ctx.saved_tensors
)
causal = ctx.causal
sm_scale = ctx.sm_scale
partition_spec = ctx.partition_spec
mesh = ctx.mesh
q_full_shape = ctx.q_full_shape
kv_full_shape = ctx.kv_full_shape
segment_ids = ctx.segment_ids
grad_q = grad_k = grad_v = grad_ab = None
grad_i = torch.sum(
o.to(torch.float32) * grad_output.to(torch.float32), axis=-1
) # [batch_size, num_heads, q_seq_len]
expanded_l = l.unsqueeze(-1).expand(
[-1 for _ in l.shape] + [FlashAttention.MIN_BLOCK_SIZE]
)
expanded_m = m.unsqueeze(-1).expand(
[-1 for _ in m.shape] + [FlashAttention.MIN_BLOCK_SIZE]
)
expanded_grad_i = grad_i.unsqueeze(-1).expand(
[-1 for _ in grad_i.shape] + [FlashAttention.MIN_BLOCK_SIZE]
)
# SPMD integration
if partition_spec is not None:
q = xs.enable_manual_sharding(q, partition_spec, mesh=mesh).global_tensor
k = xs.enable_manual_sharding(k, partition_spec, mesh=mesh).global_tensor
v = xs.enable_manual_sharding(v, partition_spec, mesh=mesh).global_tensor
expanded_l = xs.enable_manual_sharding(
expanded_l, partition_spec, mesh=mesh
).global_tensor
expanded_m = xs.enable_manual_sharding(
expanded_m, partition_spec, mesh=mesh
).global_tensor
grad_output = xs.enable_manual_sharding(
grad_output, partition_spec, mesh=mesh
).global_tensor
expanded_grad_i = xs.enable_manual_sharding(
expanded_grad_i, partition_spec, mesh=mesh
).global_tensor
if ab:
ab = xs.enable_manual_sharding(
ab, partition_spec, mesh=mesh
).global_tensor
if ctx.needs_input_grad[0]:
payload, _ = trace_pallas(
_flash_attention_bwd_dq,
q,
k,
v,
ab,
segment_ids,
l,
m,
grad_output,
grad_i,
block_q_major=min(
FlashAttention.DEFAULT_BLOCK_SIZES["block_q_dq"], q.shape[2]
),
block_k_major=min(
FlashAttention.DEFAULT_BLOCK_SIZES["block_k_major_dq"], k.shape[2]
),
block_k=min(
FlashAttention.DEFAULT_BLOCK_SIZES["block_k_dq"], k.shape[2]
),
sm_scale=sm_scale,
causal=causal,
mask_value=FlashAttention.DEFAULT_MASK_VALUE,
debug=False,
static_argnames=[
"block_q_major",
"block_k_major",
"block_k",
"sm_scale",
"causal",
"mask_value",
"debug",
],
use_cache=True,
)
args = [q, k, v]
if ab is not None:
args += [ab]
if segment_ids is not None:
args += [q_segment_ids_fa, kv_segment_ids_fa]
args += [expanded_l, expanded_m, grad_output, expanded_grad_i]
outputs = [q]
if ab is not None:
outputs += [ab]
grads = torch_xla._XLAC._xla_tpu_custom_call(
args, payload, [i.shape for i in outputs], [i.dtype for i in outputs]
)
if ctx.needs_input_grad[0]:
grad_q = grads[0]
if ctx.needs_input_grad[-3]:
grad_ab = grads[1]
if ctx.needs_input_grad[1] or ctx.needs_input_grad[2]:
payload, _ = trace_pallas(
_flash_attention_bwd_dkv,
q,
k,
v,
ab,
segment_ids,
l,
m,
grad_output,
grad_i,
block_q_major=min(
FlashAttention.DEFAULT_BLOCK_SIZES["block_q_major_dkv"], q.shape[2]
),
block_k_major=min(
FlashAttention.DEFAULT_BLOCK_SIZES["block_k_major_dkv"], k.shape[2]
),
block_k=min(
FlashAttention.DEFAULT_BLOCK_SIZES["block_k_dkv"], k.shape[2]
),
block_q=min(
FlashAttention.DEFAULT_BLOCK_SIZES["block_q_dkv"], q.shape[2]
),
sm_scale=sm_scale,
causal=causal,
mask_value=FlashAttention.DEFAULT_MASK_VALUE,
debug=False,
static_argnames=[
"block_q_major",
"block_k_major",
"block_k",
"block_q",
"sm_scale",
"causal",
"mask_value",
"debug",
],
use_cache=True,
)
grads = torch_xla._XLAC._xla_tpu_custom_call(
args, payload, [k.shape, v.shape], [k.dtype, v.dtype]
)
if ctx.needs_input_grad[1]:
grad_k = grads[0]
if ctx.needs_input_grad[2]:
grad_v = grads[1]
# SPMD integration
if partition_spec is not None:
grad_q = xs.disable_manual_sharding(
grad_q, partition_spec, q_full_shape, mesh=mesh
).global_tensor
grad_k = xs.disable_manual_sharding(
grad_k, partition_spec, kv_full_shape, mesh=mesh
).global_tensor
grad_v = xs.disable_manual_sharding(
grad_v, partition_spec, kv_full_shape, mesh=mesh
).global_tensor
return grad_q, grad_k, grad_v, None, None, None, None, grad_ab, None, None
if __name__ == "__main__":
if len(os.sys.argv) < 2:
print("Usage: python custom_kernel_spmd.py <use_spmd>")
os.sys.exit(1)
use_spmd = os.sys.argv[1]
jax.config.update("jax_default_matmul_precision", "highest")
mesh, attn_spec = None, None
if use_spmd:
import torch_xla.runtime as xr
from torch_xla.distributed.spmd import Mesh
import numpy as np
xr.use_spmd()
num_devices = xr.global_runtime_device_count()
mesh_shape = (1, 1, num_devices)
device_ids = np.array(range(num_devices))
mesh = Mesh(device_ids, mesh_shape, ("data", "model", "sequence"))
attn_spec = ("data", None, None, None)
batch_size = 1000
data_path = "data.pkl"
if os.path.exists(data_path):
with open(data_path, "rb") as f:
q, k, v, mask = pickle.load(f)
else:
q = torch.randn(batch_size, 2, 128, 4)
k = torch.randn(batch_size, 2, 128, 4)
v = torch.randn(batch_size, 2, 128, 4)
mask = torch.rand(batch_size, 128)
pickle.dump((q, k, v, mask), open(data_path, "wb"))
q, k, v, mask = q.to("xla"), k.to("xla"), v.to("xla"), mask.to("xla")
q.requires_grad = True
k.requires_grad = True
v.requires_grad = True
q.retain_grad()
k.retain_grad()
v.retain_grad()
q_segment_indexes = torch.ones(
batch_size, q.shape[2], device=q.device, dtype=torch.float32
)
grads_path = "grads.pkl"
if os.path.exists(grads_path):
print("loaded output")
with open(grads_path, "rb") as f:
o, q_grad, k_grad, v_grad = pickle.load(f)
o, q_grad, k_grad, v_grad = (
o.to("xla"),
q_grad.to("xla"),
k_grad.to("xla"),
v_grad.to("xla"),
)
else:
o = SPMDFlashAttention.apply(
q, k, v, False, q_segment_indexes, mask, 1.0, attn_spec, mesh
)
print(f"created output with shape {o.shape}", flush=True)
loss = o.sum()
loss.backward()
xm.mark_step()
q_grad = q.grad
k_grad = k.grad
v_grad = v.grad
o_cpu = o.cpu()
with open("grads.pkl", "wb") as f:
pickle.dump([o.cpu(), q_grad.cpu(), k_grad.cpu(), v_grad.cpu()], f)
q.grad = None
k.grad = None
v.grad = None
o2 = SPMDFlashAttention.apply(
q, k, v, False, q_segment_indexes, mask, 1.0, attn_spec, mesh
)
loss = o2.sum()
loss.backward()
xm.mark_step()
print(
"comparing gradients (loaded / computed) to the gradients after computing the same again:"
)
for i, j in [(q_grad, q.grad), (k_grad, k.grad), (v_grad, v.grad)]:
print(torch.allclose(i, j, rtol=1e-14))
print("opposite")
for i, j in [(q_grad, q.grad), (k_grad, k.grad), (v_grad, v.grad)]:
print(torch.allclose(j, i, rtol=1e-14))
print(f"comparing second output with shape: {o2.shape}", flush=True)
print(torch.allclose(o, o2, rtol=1e-14))
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