[PYTHON] Add Blocksparse Attention Fwd/Bwd Test (#69)
Also includes small bugfix for block-sparse softmax
This commit is contained in:
Jared Kaplan
committed by
Philippe Tillet
Philippe Tillet
parent
7aa4d080b3
commit
045ab5d62a
@@ -3,44 +3,57 @@ import triton
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import pytest
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@pytest.mark.parametrize("MODE, TRANS_A, TRANS_B, BLOCK",
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@pytest.mark.parametrize(
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"MODE, TRANS_A, TRANS_B, BLOCK",
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[
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(mode, at, bt, block) for mode in ['sdd', 'dsd', 'dds']\
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for at in [False, True]\
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for bt in [False, True]\
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for block in [16, 32, 64]
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]
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)
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def test_matmul(MODE, TRANS_A, TRANS_B, BLOCK, DTYPE=torch.float16, Z=3, H=2, M=128, N=256, K=384):
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(mode, at, bt, block)
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for mode in ["sdd", "dsd", "dds"]
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for at in [False, True]
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for bt in [False, True]
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for block in [16, 32, 64]
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],
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)
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def test_matmul(
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MODE, TRANS_A, TRANS_B, BLOCK, DTYPE=torch.float16, Z=3, H=2, M=128, N=256, K=384
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):
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# set seed
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torch.random.manual_seed(0)
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# create inputs
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a = torch.randn((Z, H, K, M) if TRANS_A else (Z, H, M, K), dtype=DTYPE, device='cuda')
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b = torch.randn((Z, H, N, K) if TRANS_B else (Z, H, K, N), dtype=DTYPE, device='cuda')
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shape = {'sdd': (M, N), 'dsd': (a.shape[2], a.shape[3]), 'dds': (b.shape[2], b.shape[3])}[MODE]
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a = torch.randn(
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(Z, H, K, M) if TRANS_A else (Z, H, M, K), dtype=DTYPE, device="cuda"
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)
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b = torch.randn(
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(Z, H, N, K) if TRANS_B else (Z, H, K, N), dtype=DTYPE, device="cuda"
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)
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shape = {
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"sdd": (M, N),
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"dsd": (a.shape[2], a.shape[3]),
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"dds": (b.shape[2], b.shape[3]),
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}[MODE]
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layout = torch.randint(2, (H, shape[0] // BLOCK, shape[1] // BLOCK))
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# triton result
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op = triton.ops.blocksparse.matmul(layout, BLOCK, MODE, trans_a=TRANS_A, trans_b=TRANS_B)
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ra = triton.testing.sparsify_tensor(a, layout, BLOCK) if MODE == 'dsd' else a
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rb = triton.testing.sparsify_tensor(b, layout, BLOCK) if MODE == 'dds' else b
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op = triton.ops.blocksparse.matmul(
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layout, BLOCK, MODE, trans_a=TRANS_A, trans_b=TRANS_B
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)
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ra = triton.testing.sparsify_tensor(a, layout, BLOCK) if MODE == "dsd" else a
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rb = triton.testing.sparsify_tensor(b, layout, BLOCK) if MODE == "dds" else b
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rc = op(ra, rb)
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# torch result
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ta = triton.testing.mask_tensor(a, layout, BLOCK) if MODE == 'dsd' else a
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tb = triton.testing.mask_tensor(b, layout, BLOCK) if MODE == 'dds' else b
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ta = triton.testing.mask_tensor(a, layout, BLOCK) if MODE == "dsd" else a
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tb = triton.testing.mask_tensor(b, layout, BLOCK) if MODE == "dds" else b
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ta = ta.transpose(2, 3) if TRANS_A else ta
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tb = tb.transpose(2, 3) if TRANS_B else tb
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tc = torch.matmul(ta, tb)
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tc = triton.testing.mask_tensor(tc, layout, BLOCK) if MODE == 'sdd' else tc
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tc = triton.testing.sparsify_tensor(tc, layout, BLOCK) if MODE == 'sdd' else tc
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tc = triton.testing.mask_tensor(tc, layout, BLOCK) if MODE == "sdd" else tc
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tc = triton.testing.sparsify_tensor(tc, layout, BLOCK) if MODE == "sdd" else tc
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# compare
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assert triton.testing.allclose(rc, tc)
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@pytest.mark.parametrize("BLOCK, WIDTH",
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[
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(block, width) for block in [32]\
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for width in [256, 576, 1024, 1792]
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]
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)
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@pytest.mark.parametrize(
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"BLOCK, WIDTH",
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[(block, width) for block in [32] for width in [256, 576, 1024, 1792]],
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)
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def test_softmax(BLOCK, WIDTH, DTYPE=torch.float16):
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# set seed
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torch.random.manual_seed(0)
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@@ -48,31 +61,125 @@ def test_softmax(BLOCK, WIDTH, DTYPE=torch.float16):
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scale = 0.4
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# create inputs
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layout = torch.randint(2, (H, M // BLOCK, N // BLOCK))
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x = torch.randn((Z, H, M, N), dtype=DTYPE, requires_grad=True, device='cuda')
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at_mask = torch.randint(low=0, high=2, size=(N, N), \
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dtype=torch.bool, requires_grad=False, device='cuda')
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kp_mask = torch.randint(low=0, high=2, size=(Z, N), \
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dtype=DTYPE, requires_grad=False, device='cuda')
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kp_mask[kp_mask == 1.] = float('-inf')
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x = torch.randn((Z, H, M, N), dtype=DTYPE, requires_grad=True, device="cuda")
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at_mask = torch.randint(
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low=0, high=2, size=(N, N), dtype=torch.bool, requires_grad=False, device="cuda"
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)
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kp_mask = torch.randint(
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low=0, high=2, size=(Z, N), dtype=DTYPE, requires_grad=False, device="cuda"
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)
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kp_mask[kp_mask == 1.0] = float("-inf")
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# triton result
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op = triton.ops.blocksparse.softmax(layout, BLOCK)
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tx = triton.testing.sparsify_tensor(x, layout, BLOCK)
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ty = op(tx,
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scale=scale,
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key_padding_mask=kp_mask,
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key_padding_mask_mode='add',
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attn_mask=at_mask.to(DTYPE),
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attn_mask_mode='mul')
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ty = op(
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tx,
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scale=scale,
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key_padding_mask=kp_mask,
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key_padding_mask_mode="add",
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attn_mask=at_mask.to(DTYPE),
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attn_mask_mode="mul",
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)
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# torch result
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rx = triton.testing.mask_tensor(x, layout, BLOCK, value=float('-inf'))
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rx = triton.testing.mask_tensor(x, layout, BLOCK, value=float("-inf"))
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if at_mask is not None:
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# broadcast at_mask to the same shape as rx
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M = at_mask[None, None, :, :] + torch.zeros_like(rx)
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rx[M == 0] = float('-inf')
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rx[M == 0] = float("-inf")
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if kp_mask is not None:
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rx += kp_mask[:, None, None, :]
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ry = torch.softmax(rx * scale, -1)
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ry = torch.softmax(rx * scale, -1)
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ry = triton.testing.sparsify_tensor(ry, layout, BLOCK)
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# compare
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assert triton.testing.allclose(ry, ty)
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assert triton.testing.allclose(ry, ty)
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def test_attention_fwd_bwd(
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input_scale=1.0,
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tol=2e-2,
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scale=1 / 8.0,
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n_ctx=256,
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dtype=torch.float16,
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batch_size=2,
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n_heads=2,
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block=64,
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):
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# inputs
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qkv_shape = (batch_size, n_heads, n_ctx, 64)
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qkvs = [
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torch.nn.Parameter(input_scale * torch.randn(qkv_shape), requires_grad=True)
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.to(dtype)
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.cuda()
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for _ in range(3)
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]
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attn_mask = torch.tril(
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torch.ones(
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[n_ctx, n_ctx],
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device="cuda",
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dtype=dtype,
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),
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diagonal=0,
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)
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# Triton:
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n_blocks = n_ctx // block
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layout = torch.tril(torch.ones([n_heads, n_blocks, n_blocks], dtype=torch.long))
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query, key, value = [x.clone() for x in qkvs]
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query.retain_grad()
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key.retain_grad()
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value.retain_grad()
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attn_out = triton_attention(
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layout, block, attn_mask, query=query, key=key, value=value, scale=scale
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)
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# ad hoc loss
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loss = (attn_out ** 2).mean()
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loss.backward()
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grads = [query.grad, key.grad, value.grad]
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# Torch version:
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torch_q, torch_k, torch_v = [x.clone() for x in qkvs]
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attn_mask = 1e6 * (-1 + (attn_mask.reshape((1, 1, n_ctx, n_ctx)).cuda()))
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torch_q.retain_grad()
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torch_k.retain_grad()
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torch_v.retain_grad()
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scores = scale * torch.einsum("bhsd,bhtd->bhst", torch_q, torch_k)
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scores = scores + attn_mask
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probs = torch.softmax(scores, dim=-1)
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torch_attn_out = torch.einsum("bhst,bhtd->bhsd", probs, torch_v)
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# ad hoc loss
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torch_loss = (torch_attn_out ** 2).mean()
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torch_loss.backward()
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torch_grads = [torch_q.grad, torch_k.grad, torch_v.grad]
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# comparison
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print(f"Triton loss {loss} and torch loss {torch_loss}. Also checking grads...")
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torch.testing.assert_allclose(loss, torch_loss, rtol=tol, atol=tol)
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for g1, g2 in zip(grads, torch_grads):
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torch.testing.assert_allclose(g1, g2, rtol=tol, atol=tol)
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def triton_attention(
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layout,
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block: int,
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attn_mask: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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scale: float,
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):
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sparse_dot_sdd_nt = triton.ops.blocksparse.matmul(
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layout, block, "sdd", trans_a=False, trans_b=True
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)
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sparse_dot_dsd_nn = triton.ops.blocksparse.matmul(
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layout, block, "dsd", trans_a=False, trans_b=False
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)
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sparse_softmax = triton.ops.blocksparse.softmax(
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layout,
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block,
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)
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w = sparse_dot_sdd_nt(query, key)
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w = sparse_softmax(w, scale=scale, attn_mask=attn_mask, attn_mask_mode="mul")
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a = sparse_dot_dsd_nn(w, value)
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return a
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@@ -126,7 +126,7 @@ class _matmul(torch.autograd.Function):
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num_lock = 1
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key = (block, device, a.dtype, b.dtype, trans_a, trans_b, trans_c, pack, is_32_multiple, is_64_multiple)
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if key not in _matmul.sdd_cache:
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defines = {'TM': block*pack, 'TN': block*pack,
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defines = {'TM': block*pack, 'TN': block*pack,
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'TMN': block*block*pack*pack,
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'BLOCK': block,
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'TK': 32,
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@@ -110,7 +110,7 @@ class _softmax(torch.autograd.Function):
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kernel = _softmax.make_kernel(fwd_kernels, fwd_src, maxlut * block, x.device, x.dtype, block, apply_scale,
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apply_rpe, apply_kp_mask, apply_attn_mask, kp_mask_mode, attn_mask_mode)
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M = x.shape[0]
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grid = lambda opt: [triton.cdiv(spdims[0] * spdims[1] * block, opt.TM), M]
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grid = lambda opt: [spdims[0] * spdims[1] * block, M]
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# run kernel
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kernel(x.data_ptr(),
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@@ -151,7 +151,7 @@ class _softmax(torch.autograd.Function):
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ctx.apply_scale, ctx.apply_rpe, ctx.apply_kp_mask, ctx.apply_attn_mask,
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ctx.kp_mask_mode, ctx.attn_mask_mode)
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M = x.shape[0]
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grid = lambda opt: [triton.cdiv(ctx.spdims[0] * ctx.spdims[1] * ctx.block, opt.TM), M]
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grid = lambda opt: [ctx.spdims[0] * ctx.spdims[1] * ctx.block, M]
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kernel(x.data_ptr(), ctx.scale, dx.data_ptr(), lut.data_ptr(), ctx.maxlut, x.stride(0), dx.stride(0), grid=grid)
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return dx, None, None, None, None, None, None, None, None, None, None, None, None, None, None
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