164 lines
6.2 KiB
Python
164 lines
6.2 KiB
Python
import torch
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import triton
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import triton.language as tl
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from .matmul_perf_model import early_config_prune, estimate_matmul_time
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def init_to_zero(name):
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return lambda nargs: nargs[name].zero_()
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def get_configs_io_bound():
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configs = []
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for num_stages in [1]:
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# TODO support block size 16 for MFMA dot op
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for block_m in [16, 32] if torch.version.hip is None and not hasattr(torch, "corex") else [32, 64]:
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for block_k in [32, 64]:
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for block_n in [32, 64, 128, 256]:
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num_warps = 4 if block_n <= 64 else 8
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configs.append(
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triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
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num_stages=num_stages, num_warps=num_warps))
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# split_k
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#for split_k in [2, 4, 8, 16]:
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# configs.append(triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k},
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# num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C')))
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return configs
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def get_configs_compute_bound():
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configs = []
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for block_m in [64, 128, 256]:
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for block_n in [64, 128, 256]:
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for block_k in [32, 64, 128]:
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num_warps = 8 if block_n <= 64 else 16
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configs.append(
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triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
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num_stages=1, num_warps=num_warps))
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return configs
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@triton.autotune(
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configs=[
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] + get_configs_compute_bound() + get_configs_io_bound(),
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key=['M', 'N', 'K'],
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prune_configs_by={
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'early_config_prune': early_config_prune,
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'perf_model': estimate_matmul_time,
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'top_k': 10
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},
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)
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@triton.heuristics({
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'EVEN_K': lambda args: args['K'] % args['BLOCK_K'] == 0,
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})
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@triton.jit
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def _bmm_kernel(A, B, C, M, N, K,
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stride_aq, stride_am, stride_ak,
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stride_bq, stride_bk, stride_bn,
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stride_cq, stride_cm, stride_cn,
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dot_out_dtype: tl.constexpr,
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BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
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GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
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):
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pid = tl.program_id(0)
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grid_m = (M + BLOCK_M - 1) // BLOCK_M
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grid_n = (N + BLOCK_N - 1) // BLOCK_N
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# re-order program ID for better L2 performance
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width = GROUP_M * grid_n
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group_id = pid // width
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group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
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pid_m = group_id * GROUP_M + (pid % group_size)
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pid_n = (pid % width) // (group_size)
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rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
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rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
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ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
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rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
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rk = tl.arange(0, BLOCK_K)
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idx_q = tl.program_id(1) # batch dimension for BMM
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A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak + idx_q*stride_aq)
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B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn + idx_q*stride_bq)
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acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=dot_out_dtype)
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for k in range(K, 0, -BLOCK_K):
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if EVEN_K:
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a = tl.load(A)
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b = tl.load(B)
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else:
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a = tl.load(A, mask=rk[None, :] < k, other=0.)
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b = tl.load(B, mask=rk[:, None] < k, other=0.)
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acc += tl.dot(a, b)
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A += BLOCK_K * stride_ak
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B += BLOCK_K * stride_bk
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# rematerialize rm and rn to save registers
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rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
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rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
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idx_q = tl.program_id(1) # batch dimension for BMM
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idx_m = rm[:, None]
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idx_n = rn[None, :]
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C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn + idx_q * stride_cq)
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mask = (idx_m < M) & (idx_n < N)
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# handles write-back with reduction-splitting
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tl.store(C, acc, mask=mask)
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class _bmm(torch.autograd.Function):
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kernel = _bmm_kernel
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_locks = {}
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@staticmethod
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def _call(a, b, dot_out_dtype):
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device = a.device
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# handle non-contiguous inputs if necessary
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if a.stride(0) > 1 and a.stride(1) > 1:
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a = a.contiguous()
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if b.stride(0) > 1 and b.stride(1) > 1:
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b = b.contiguous()
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#only MR support Trans layout
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if hasattr(torch, "corex"):
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capability = torch.cuda.get_device_capability(device)
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capability = capability[0] * 10 + capability[1]
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if (capability < 71):
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if a.stride(0) >= 1 and a.stride(1) > 1:
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a = a.contiguous()
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if b.stride(0) >= 1 and b.stride(1) > 1:
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b = b.contiguous()
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# checks constraints
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assert a.shape[0] == b.shape[0], "incompatible dimensions"
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assert a.shape[2] == b.shape[1], "incompatible dimensions"
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B, M, K = a.shape
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_, _, N = b.shape
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# allocates output
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c = torch.empty((B, M, N), device=device, dtype=a.dtype)
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if dot_out_dtype is None:
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if a.dtype in [torch.float16, torch.float32, torch.bfloat16]:
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dot_out_dtype = tl.float32
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else:
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dot_out_dtype = tl.int32
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else:
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assert isinstance(dot_out_dtype, torch.dtype), "dot_out_dtype must be a torch.dtype"
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if dot_out_dtype == torch.float16:
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dot_out_dtype = tl.float16
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elif dot_out_dtype in [torch.float32, torch.bfloat16]:
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dot_out_dtype = tl.float32
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else:
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dot_out_dtype = tl.int32
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# launch kernel
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grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), B, 1)
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_bmm_kernel[grid](a, b, c, M, N, K,
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a.stride(0), a.stride(1), a.stride(2),
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b.stride(0), b.stride(1), b.stride(2),
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c.stride(0), c.stride(1), c.stride(2),
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dot_out_dtype=dot_out_dtype,
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GROUP_M=8)
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return c
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@staticmethod
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def forward(ctx, a, b, dot_out_dtype=None):
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return _bmm._call(a, b, dot_out_dtype=dot_out_dtype)
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bmm = _bmm.apply
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