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import torch
import triton
import triton.language as tl
def hunyuan_token_reorder_to_token_major(tensor, fix_len, reorder_len, reorder_num_frame, frame_size):
"""Reorder it from frame major to token major!"""
assert reorder_len == reorder_num_frame * frame_size
assert tensor.shape[2] == fix_len + reorder_len
tensor[:, :, :-fix_len, :] = tensor[:, :, :-fix_len:, :].reshape(tensor.shape[0], tensor.shape[1], reorder_num_frame, frame_size, tensor.shape[3]) \
.transpose(2, 3).reshape(tensor.shape[0], tensor.shape[1], reorder_len, tensor.shape[3])
return tensor
def hunyuan_token_reorder_to_frame_major(tensor, fix_len, reorder_len, reorder_num_frame, frame_size):
"""Reorder it from token major to frame major!"""
assert reorder_len == reorder_num_frame * frame_size
assert tensor.shape[2] == fix_len + reorder_len
tensor[:, :, :-fix_len:, :] = tensor[:, :, :-fix_len:, :].reshape(tensor.shape[0], tensor.shape[1], frame_size, reorder_num_frame, tensor.shape[3]) \
.transpose(2, 3).reshape(tensor.shape[0], tensor.shape[1], reorder_len, tensor.shape[3])
return tensor
@triton.jit
def hunyuan_sparse_head_placement_kernel(
query_ptr, key_ptr, value_ptr, # [cfg, num_heads, seq_len, head_dim] seq_len = context_length + num_frame * frame_size
query_out_ptr, key_out_ptr, value_out_ptr, # [cfg, num_heads, seq_len, head_dim]
best_mask_idx_ptr, # [cfg, num_heads]
query_stride_b, query_stride_h, query_stride_s, query_stride_d,
mask_idx_stride_b, mask_idx_stride_h,
seq_len: tl.constexpr,
head_dim: tl.constexpr,
context_length: tl.constexpr,
num_frame: tl.constexpr,
frame_size: tl.constexpr,
BLOCK_SIZE: tl.constexpr
):
# Copy query, key, value to output
# range: [b, h, block_id * block_size: block_id * block_size + block_size, :]
cfg = tl.program_id(0)
head = tl.program_id(1)
block_id = tl.program_id(2)
start_id = block_id * BLOCK_SIZE
end_id = start_id + BLOCK_SIZE
end_id = tl.where(end_id > seq_len, seq_len, end_id)
# Load best mask idx (0 is spatial, 1 is temporal)
is_temporal = tl.load(best_mask_idx_ptr + cfg * mask_idx_stride_b + head * mask_idx_stride_h)
offset_token = tl.arange(0, BLOCK_SIZE) + start_id
offset_mask = offset_token < seq_len
offset_d = tl.arange(0, head_dim)
if is_temporal:
frame_id = offset_token // frame_size
patch_id = offset_token - frame_id * frame_size
offset_store_token = tl.where(offset_token >= seq_len - context_length, offset_token, patch_id * num_frame + frame_id)
offset_load = (cfg * query_stride_b + head * query_stride_h + offset_token[:,None] * query_stride_s) + offset_d[None,:] * query_stride_d
offset_query = query_ptr + offset_load
offset_key = key_ptr + offset_load
offset_value = value_ptr + offset_load
offset_store = (cfg * query_stride_b + head * query_stride_h + offset_store_token[:,None] * query_stride_s) + offset_d[None,:] * query_stride_d
offset_query_out = query_out_ptr + offset_store
offset_key_out = key_out_ptr + offset_store
offset_value_out = value_out_ptr + offset_store
# Maybe tune the pipeline here
query = tl.load(offset_query, mask=offset_mask[:,None])
tl.store(offset_query_out, query, mask=offset_mask[:,None])
key = tl.load(offset_key, mask=offset_mask[:,None])
tl.store(offset_key_out, key, mask=offset_mask[:,None])
value = tl.load(offset_value, mask=offset_mask[:,None])
tl.store(offset_value_out, value, mask=offset_mask[:,None])
else:
offset_load = (cfg * query_stride_b + head * query_stride_h + offset_token[:,None] * query_stride_s) + offset_d[None,:] * query_stride_d
offset_query = query_ptr + offset_load
offset_key = key_ptr + offset_load
offset_value = value_ptr + offset_load
offset_store = offset_load
offset_query_out = query_out_ptr + offset_store
offset_key_out = key_out_ptr + offset_store
offset_value_out = value_out_ptr + offset_store
# Maybe tune the pipeline here
query = tl.load(offset_query, mask=offset_mask[:,None])
tl.store(offset_query_out, query, mask=offset_mask[:,None])
key = tl.load(offset_key, mask=offset_mask[:,None])
tl.store(offset_key_out, key, mask=offset_mask[:,None])
value = tl.load(offset_value, mask=offset_mask[:,None])
tl.store(offset_value_out, value, mask=offset_mask[:,None])
def hunyuan_sparse_head_placement(query, key, value, query_out, key_out, value_out, best_mask_idx, context_length, num_frame, frame_size):
cfg, num_heads, seq_len, head_dim = query.shape
BLOCK_SIZE = 128
assert seq_len == context_length + num_frame * frame_size
grid = (cfg, num_heads, (seq_len + BLOCK_SIZE - 1) // BLOCK_SIZE)
hunyuan_sparse_head_placement_kernel[grid](
query, key, value,
query_out, key_out, value_out,
best_mask_idx,
query.stride(0), query.stride(1), query.stride(2), query.stride(3),
best_mask_idx.stride(0), best_mask_idx.stride(1),
seq_len, head_dim, context_length, num_frame, frame_size,
BLOCK_SIZE
)
def ref_hunyuan_sparse_head_placement(query, key, value, best_mask_idx, context_length, num_frame, frame_size):
cfg, num_heads, seq_len, head_dim = query.shape
assert seq_len == context_length + num_frame * frame_size
query_out = query.clone()
key_out = key.clone()
value_out = value.clone()
# Spatial
query_out[best_mask_idx == 0], key_out[best_mask_idx == 0], value_out[best_mask_idx == 0] = \
query[best_mask_idx == 0], key[best_mask_idx == 0], value[best_mask_idx == 0]
# Temporal
query_out[best_mask_idx == 1], key_out[best_mask_idx == 1], value_out[best_mask_idx == 1] = \
hunyuan_token_reorder_to_token_major(query[best_mask_idx == 1].unsqueeze(0), context_length, num_frame * frame_size, num_frame, frame_size).squeeze(0), \
hunyuan_token_reorder_to_token_major(key[best_mask_idx == 1].unsqueeze(0), context_length, num_frame * frame_size, num_frame, frame_size).squeeze(0), \
hunyuan_token_reorder_to_token_major(value[best_mask_idx == 1].unsqueeze(0), context_length, num_frame * frame_size, num_frame, frame_size).squeeze(0)
return query_out, key_out, value_out
def test_hunyuan_sparse_head_placement():
context_length = 226
num_frame = 11
frame_size = 4080
cfg = 2
num_heads = 48
seq_len = context_length + num_frame * frame_size
head_dim = 64
dtype = torch.bfloat16
device = torch.device("cuda")
query = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device)
key = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device)
value = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device)
best_mask_idx = torch.randint(0, 2, (cfg, num_heads), device=device)
query_out = torch.empty_like(query)
key_out = torch.empty_like(key)
value_out = torch.empty_like(value)
hunyuan_sparse_head_placement(query, key, value, query_out, key_out, value_out, best_mask_idx, context_length, num_frame, frame_size)
ref_query_out, ref_key_out, ref_value_out = ref_hunyuan_sparse_head_placement(query, key, value, best_mask_idx, context_length, num_frame, frame_size)
torch.testing.assert_close(query_out, ref_query_out)
torch.testing.assert_close(key_out, ref_key_out)
torch.testing.assert_close(value_out, ref_value_out)
def benchmark_hunyuan_sparse_head_placement():
import time
context_length = 226
num_frame = 11
frame_size = 4080
cfg = 2
num_heads = 48
seq_len = context_length + num_frame * frame_size
head_dim = 64
dtype = torch.bfloat16
device = torch.device("cuda")
query = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device)
key = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device)
value = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device)
best_mask_idx = torch.randint(0, 2, (cfg, num_heads), device=device)
query_out = torch.empty_like(query)
key_out = torch.empty_like(key)
value_out = torch.empty_like(value)
warmup = 10
all_iter = 1000
# warmup
for _ in range(warmup):
hunyuan_sparse_head_placement(query, key, value, query_out, key_out, value_out, best_mask_idx, context_length, num_frame, frame_size)
torch.cuda.synchronize()
start = time.time()
for _ in range(all_iter):
hunyuan_sparse_head_placement(query, key, value, query_out, key_out, value_out, best_mask_idx, context_length, num_frame, frame_size)
torch.cuda.synchronize()
end = time.time()
print(f"Triton Elapsed Time: {(end - start) / all_iter * 1e3:.2f} ms")
print(f"Triton Total Bandwidth: {query.nelement() * query.element_size() * 3 * 2 * all_iter / (end - start) / 1e9:.2f} GB/s")
torch.cuda.synchronize()
start = time.time()
for _ in range(all_iter):
ref_hunyuan_sparse_head_placement(query, key, value, best_mask_idx, context_length, num_frame, frame_size)
torch.cuda.synchronize()
end = time.time()
print(f"Reference Elapsed Time: {(end - start) / all_iter * 1e3:.2f} ms")
print(f"Reference Total Bandwidth: {query.nelement() * query.element_size() * 3 * 2 * all_iter / (end - start) / 1e9:.2f} GB/s")
@triton.jit
def hunyuan_hidden_states_placement_kernel(
hidden_states_ptr, # [cfg, num_heads, seq_len, head_dim] seq_len = context_length + num_frame * frame_size
hidden_states_out_ptr, # [cfg, num_heads, seq_len, head_dim]
best_mask_idx_ptr, # [cfg, num_heads]
hidden_states_stride_b, hidden_states_stride_h, hidden_states_stride_s, hidden_states_stride_d,
mask_idx_stride_b, mask_idx_stride_h,
seq_len: tl.constexpr,
head_dim: tl.constexpr,
context_length: tl.constexpr,
num_frame: tl.constexpr,
frame_size: tl.constexpr,
BLOCK_SIZE: tl.constexpr
):
# Copy hidden_states to output
# range: [b, h, block_id * block_size: block_id * block_size + block_size, :]
cfg = tl.program_id(0)
head = tl.program_id(1)
block_id = tl.program_id(2)
start_id = block_id * BLOCK_SIZE
end_id = start_id + BLOCK_SIZE
end_id = tl.where(end_id > seq_len, seq_len, end_id)
# Load best mask idx (0 is spatial, 1 is temporal)
is_temporal = tl.load(best_mask_idx_ptr + cfg * mask_idx_stride_b + head * mask_idx_stride_h)
offset_token = tl.arange(0, BLOCK_SIZE) + start_id
offset_mask = offset_token < seq_len
offset_d = tl.arange(0, head_dim)
if is_temporal:
patch_id = offset_token // num_frame
frame_id = offset_token - patch_id * num_frame
offset_store_token = tl.where(offset_token >= seq_len - context_length, offset_token, frame_id * frame_size + patch_id)
offset_load = (cfg * hidden_states_stride_b + head * hidden_states_stride_h + offset_token[:,None] * hidden_states_stride_s) + offset_d[None,:] * hidden_states_stride_d
offset_hidden_states = hidden_states_ptr + offset_load
offset_store = (cfg * hidden_states_stride_b + head * hidden_states_stride_h + offset_store_token[:,None] * hidden_states_stride_s) + offset_d[None,:] * hidden_states_stride_d
offset_hidden_states_out = hidden_states_out_ptr + offset_store
# Maybe tune the pipeline here
hidden_states = tl.load(offset_hidden_states, mask=offset_mask[:,None])
tl.store(offset_hidden_states_out, hidden_states, mask=offset_mask[:,None])
else:
offset_load = (cfg * hidden_states_stride_b + head * hidden_states_stride_h + offset_token[:,None] * hidden_states_stride_s) + offset_d[None,:] * hidden_states_stride_d
offset_hidden_states = hidden_states_ptr + offset_load
offset_store = offset_load
offset_hidden_states_out = hidden_states_out_ptr + offset_store
# Maybe tune the pipeline here
hidden_states = tl.load(offset_hidden_states, mask=offset_mask[:,None])
tl.store(offset_hidden_states_out, hidden_states, mask=offset_mask[:,None])
def hunyuan_hidden_states_placement(hidden_states, hidden_states_out, best_mask_idx, context_length, num_frame, frame_size):
cfg, num_heads, seq_len, head_dim = hidden_states.shape
BLOCK_SIZE = 128
assert seq_len == context_length + num_frame * frame_size
grid = (cfg, num_heads, (seq_len + BLOCK_SIZE - 1) // BLOCK_SIZE)
hunyuan_hidden_states_placement_kernel[grid](
hidden_states,
hidden_states_out,
best_mask_idx,
hidden_states.stride(0), hidden_states.stride(1), hidden_states.stride(2), hidden_states.stride(3),
best_mask_idx.stride(0), best_mask_idx.stride(1),
seq_len, head_dim, context_length, num_frame, frame_size,
BLOCK_SIZE
)
return hidden_states_out
def ref_hunyuan_hidden_states_placement(hidden_states, output_hidden_states, best_mask_idx, context_length, num_frame, frame_size):
cfg, num_heads, seq_len, head_dim = hidden_states.shape
assert seq_len == context_length + num_frame * frame_size
# Spatial
output_hidden_states[best_mask_idx == 0] = hidden_states[best_mask_idx == 0]
# Temporal
output_hidden_states[best_mask_idx == 1] = hunyuan_token_reorder_to_frame_major(hidden_states[best_mask_idx == 1].unsqueeze(0), context_length, num_frame * frame_size, num_frame, frame_size).squeeze(0)
def test_hunyuan_hidden_states_placement():
context_length = 226
num_frame = 11
frame_size = 4080
cfg = 2
num_heads = 48
seq_len = context_length + num_frame * frame_size
head_dim = 64
dtype = torch.bfloat16
device = torch.device("cuda")
hidden_states = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device)
best_mask_idx = torch.randint(0, 2, (cfg, num_heads), device=device)
hidden_states_out1 = torch.empty_like(hidden_states)
hidden_states_out2 = torch.empty_like(hidden_states)
hunyuan_hidden_states_placement(hidden_states, hidden_states_out1, best_mask_idx, context_length, num_frame, frame_size)
ref_hunyuan_hidden_states_placement(hidden_states, hidden_states_out2, best_mask_idx, context_length, num_frame, frame_size)
torch.testing.assert_close(hidden_states_out1, hidden_states_out2)
def benchmark_hunyuan_hidden_states_placement():
import time
context_length = 226
num_frame = 11
frame_size = 4080
cfg = 2
num_heads = 48
seq_len = context_length + num_frame * frame_size
head_dim = 64
dtype = torch.bfloat16
device = torch.device("cuda")
hidden_states = torch.randn(cfg, num_heads, seq_len, head_dim, dtype=dtype, device=device)
best_mask_idx = torch.randint(0, 2, (cfg, num_heads), device=device)
hidden_states_out = torch.empty_like(hidden_states)
warmup = 10
all_iter = 1000
# warmup
for _ in range(warmup):
hunyuan_hidden_states_placement(hidden_states, hidden_states_out, best_mask_idx, context_length, num_frame, frame_size)
torch.cuda.synchronize()
start = time.time()
for _ in range(all_iter):
hunyuan_hidden_states_placement(hidden_states, hidden_states_out, best_mask_idx, context_length, num_frame, frame_size)
torch.cuda.synchronize()
end = time.time()
print(f"Triton Elapsed Time: {(end - start) / all_iter * 1e3:.2f} ms")
print(f"Triton Total Bandwidth: {hidden_states.nelement() * hidden_states.element_size() * 2 * all_iter / (end - start) / 1e9:.2f} GB/s")
torch.cuda.synchronize()
start = time.time()
for _ in range(all_iter):
ref_hunyuan_hidden_states_placement(hidden_states, hidden_states.clone(), best_mask_idx, context_length, num_frame, frame_size)
torch.cuda.synchronize()
end = time.time()
print(f"Reference Elapsed Time: {(end - start) / all_iter * 1e3:.2f} ms")
print(f"Reference Total Bandwidth: {hidden_states.nelement() * hidden_states.element_size() * 2 * all_iter / (end - start) / 1e9:.2f} GB/s")
if __name__ == "__main__":
test_hunyuan_sparse_head_placement()
benchmark_hunyuan_sparse_head_placement()
test_hunyuan_hidden_states_placement()
benchmark_hunyuan_hidden_states_placement()
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