fix bug
This commit is contained in:
John Smith
parent
86387a0a35
commit
085d9556f9
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@ -48,7 +48,7 @@ class Autograd4bitQuantLinear(nn.Module):
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)
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self.register_buffer('scales', torch.empty((math.ceil(infeatures/groupsize), outfeatures)))
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self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(infeatures)], dtype = torch.int32))
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self.bias = nn.Parameter(torch.empty(outfeatures))
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self.register_buffer('bias', torch.empty(outfeatures))
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self.register_buffer(
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'qweight', torch.empty((infeatures // 256 * (bits * 8), outfeatures), dtype=torch.int)
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)
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@ -58,11 +58,11 @@ class Autograd4bitQuantLinear(nn.Module):
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if torch.is_grad_enabled():
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out = AutogradMatmul4bit.apply(x, self.qweight, self.scales,
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self.qzeros if self.groupsize != -1 else self.zeros, self.groupsize)
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out.add_(self.bias)
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out += self.bias
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else:
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out = mm4b.matmul4bit(x, self.qweight, self.scales,
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self.qzeros if self.groupsize != -1 else self.zeros, self.groupsize)
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out.add_(self.bias)
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out += self.bias
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return out
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408
triton_utils.py
408
triton_utils.py
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@ -1,205 +1,205 @@
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import triton
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import triton.language as tl
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import torch
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# code based https://github.com/fpgaminer/GPTQ-triton
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@triton.autotune(
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configs=[
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
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triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
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triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
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],
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key=['M', 'N', 'K'],
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)
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@triton.jit
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def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
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scales_ptr, zeros_ptr, g_ptr,
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M, N, K, bits, maxq,
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stride_am, stride_ak,
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stride_bk, stride_bn,
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stride_cm, stride_cn,
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stride_scales, stride_zeros,
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BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
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GROUP_SIZE_M: tl.constexpr):
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"""
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Compute the matrix multiplication C = A x B.
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A is of shape (M, K) float16
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B is of shape (K//8, N) int32
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C is of shape (M, N) float16
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scales is of shape (G, N) float16
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zeros is of shape (G, N) float16
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g_ptr is of shape (K) int32
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"""
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infearure_per_bits = 32 // bits
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pid = tl.program_id(axis=0)
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num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
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num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
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num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
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num_pid_in_group = GROUP_SIZE_M * num_pid_n
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group_id = pid // num_pid_in_group
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first_pid_m = group_id * GROUP_SIZE_M
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group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
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pid_m = first_pid_m + (pid % group_size_m)
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pid_n = (pid % num_pid_in_group) // group_size_m
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offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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offs_k = tl.arange(0, BLOCK_SIZE_K)
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a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
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a_mask = (offs_am[:, None] < M)
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# b_ptrs is set up such that it repeats elements along the K axis 8 times
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b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
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g_ptrs = g_ptr + offs_k
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# shifter is used to extract the N bits of each element in the 32-bit word from B
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scales_ptrs = scales_ptr + offs_bn[None, :]
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zeros_ptrs = zeros_ptr + (offs_bn[None, :]// infearure_per_bits)
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shifter = (offs_k % infearure_per_bits) * bits
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zeros_shifter = (offs_bn % infearure_per_bits) * bits
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accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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for k in range(0, num_pid_k):
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g_idx = tl.load(g_ptrs)
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# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
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scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = (zeros >> zeros_shifter[None, :]) & maxq
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zeros = (zeros + 1)
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a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
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b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
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# Now we need to unpack b (which is N-bit values) into 32-bit values
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b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
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b = (b - zeros) * scales # Scale and shift
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accumulator += tl.dot(a, b)
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a_ptrs += BLOCK_SIZE_K
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b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
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g_ptrs += BLOCK_SIZE_K
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c = accumulator.to(tl.float16)
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c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
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c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
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tl.store(c_ptrs, accumulator, mask=c_mask)
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# code based https://github.com/fpgaminer/GPTQ-triton
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@triton.autotune(
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configs=[
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
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triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
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triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
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],
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key=['M', 'N', 'K'],
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)
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@triton.jit
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def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
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scales_ptr, zeros_ptr, g_ptr,
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M, N, K, bits, maxq,
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stride_am, stride_ak,
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stride_bk, stride_bn,
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stride_cm, stride_cn,
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stride_scales, stride_zeros,
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BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
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GROUP_SIZE_M: tl.constexpr):
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"""
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Compute the matrix multiplication C = A x B.
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A is of shape (M, N) float16
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B is of shape (K//8, N) int32
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C is of shape (M, K) float16
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scales is of shape (G, N) float16
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zeros is of shape (G, N) float16
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g_ptr is of shape (K) int32
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"""
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infearure_per_bits = 32 // bits
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pid = tl.program_id(axis=0)
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num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
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num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
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num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
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num_pid_in_group = GROUP_SIZE_M * num_pid_k
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group_id = pid // num_pid_in_group
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first_pid_m = group_id * GROUP_SIZE_M
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group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
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pid_m = first_pid_m + (pid % group_size_m)
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pid_k = (pid % num_pid_in_group) // group_size_m
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offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
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offs_n = tl.arange(0, BLOCK_SIZE_N)
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a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
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a_mask = (offs_am[:, None] < M)
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# b_ptrs is set up such that it repeats elements along the K axis 8 times
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b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
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g_ptrs = g_ptr + offs_bk
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g_idx = tl.load(g_ptrs)
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# shifter is used to extract the N bits of each element in the 32-bit word from B
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scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales
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zeros_ptrs = zeros_ptr + (offs_n[None, :]// infearure_per_bits) + g_idx[:, None] * stride_zeros
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shifter = (offs_bk % infearure_per_bits) * bits
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zeros_shifter = (offs_n % infearure_per_bits) * bits
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accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)
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for k in range(0, num_pid_n):
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# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
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scales = tl.load(scales_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = (zeros >> zeros_shifter[None, :]) & maxq
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zeros = (zeros + 1)
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a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
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b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
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# Now we need to unpack b (which is N-bit values) into 32-bit values
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b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
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b = (b - zeros) * scales # Scale and shift
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b = tl.trans(b)
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accumulator += tl.dot(a, b)
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a_ptrs += BLOCK_SIZE_N
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b_ptrs += BLOCK_SIZE_N
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scales_ptrs += BLOCK_SIZE_N
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zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
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c = accumulator.to(tl.float16)
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c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
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c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
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tl.store(c_ptrs, accumulator, mask=c_mask)
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def triton_matmul(input, qweight, scales, qzeros, g_idx, bits, maxq):
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output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
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grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
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matmul_248_kernel[grid](input, qweight, output,
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scales, qzeros, g_idx,
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input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
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input.stride(0), input.stride(1),
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qweight.stride(0), qweight.stride(1),
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output.stride(0), output.stride(1),
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scales.stride(0), qzeros.stride(0))
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return output
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import triton
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import triton.language as tl
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import torch
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# code based https://github.com/fpgaminer/GPTQ-triton
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@triton.autotune(
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configs=[
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
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triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
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triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
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],
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key=['M', 'N', 'K'],
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)
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@triton.jit
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def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
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scales_ptr, zeros_ptr, g_ptr,
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M, N, K, bits, maxq,
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stride_am, stride_ak,
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stride_bk, stride_bn,
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stride_cm, stride_cn,
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stride_scales, stride_zeros,
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BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
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GROUP_SIZE_M: tl.constexpr):
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"""
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Compute the matrix multiplication C = A x B.
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A is of shape (M, K) float16
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B is of shape (K//8, N) int32
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C is of shape (M, N) float16
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scales is of shape (G, N) float16
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zeros is of shape (G, N) float16
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g_ptr is of shape (K) int32
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"""
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infearure_per_bits = 32 // bits
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pid = tl.program_id(axis=0)
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num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
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num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
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num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
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num_pid_in_group = GROUP_SIZE_M * num_pid_n
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group_id = pid // num_pid_in_group
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first_pid_m = group_id * GROUP_SIZE_M
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group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
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pid_m = first_pid_m + (pid % group_size_m)
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pid_n = (pid % num_pid_in_group) // group_size_m
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offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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offs_k = tl.arange(0, BLOCK_SIZE_K)
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a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
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a_mask = (offs_am[:, None] < M)
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# b_ptrs is set up such that it repeats elements along the K axis 8 times
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b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
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g_ptrs = g_ptr + offs_k
|
||||
# shifter is used to extract the N bits of each element in the 32-bit word from B
|
||||
scales_ptrs = scales_ptr + offs_bn[None, :]
|
||||
zeros_ptrs = zeros_ptr + (offs_bn[None, :]// infearure_per_bits)
|
||||
|
||||
shifter = (offs_k % infearure_per_bits) * bits
|
||||
zeros_shifter = (offs_bn % infearure_per_bits) * bits
|
||||
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
|
||||
|
||||
for k in range(0, num_pid_k):
|
||||
g_idx = tl.load(g_ptrs)
|
||||
|
||||
# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
|
||||
scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
|
||||
zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
|
||||
|
||||
zeros = (zeros >> zeros_shifter[None, :]) & maxq
|
||||
zeros = (zeros + 1)
|
||||
|
||||
a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
|
||||
b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
|
||||
|
||||
# Now we need to unpack b (which is N-bit values) into 32-bit values
|
||||
b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
|
||||
b = (b - zeros) * scales # Scale and shift
|
||||
|
||||
accumulator += tl.dot(a, b)
|
||||
a_ptrs += BLOCK_SIZE_K
|
||||
b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
|
||||
g_ptrs += BLOCK_SIZE_K
|
||||
|
||||
c = accumulator.to(tl.float16)
|
||||
c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
|
||||
c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
|
||||
tl.store(c_ptrs, accumulator, mask=c_mask)
|
||||
|
||||
# code based https://github.com/fpgaminer/GPTQ-triton
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
|
||||
triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
|
||||
],
|
||||
key=['M', 'N', 'K'],
|
||||
)
|
||||
|
||||
@triton.jit
|
||||
def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
|
||||
scales_ptr, zeros_ptr, g_ptr,
|
||||
M, N, K, bits, maxq,
|
||||
stride_am, stride_ak,
|
||||
stride_bk, stride_bn,
|
||||
stride_cm, stride_cn,
|
||||
stride_scales, stride_zeros,
|
||||
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
|
||||
GROUP_SIZE_M: tl.constexpr):
|
||||
"""
|
||||
Compute the matrix multiplication C = A x B.
|
||||
A is of shape (M, N) float16
|
||||
B is of shape (K//8, N) int32
|
||||
C is of shape (M, K) float16
|
||||
scales is of shape (G, N) float16
|
||||
zeros is of shape (G, N) float16
|
||||
g_ptr is of shape (K) int32
|
||||
"""
|
||||
infearure_per_bits = 32 // bits
|
||||
|
||||
pid = tl.program_id(axis=0)
|
||||
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
|
||||
num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
|
||||
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
|
||||
num_pid_in_group = GROUP_SIZE_M * num_pid_k
|
||||
group_id = pid // num_pid_in_group
|
||||
first_pid_m = group_id * GROUP_SIZE_M
|
||||
group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
|
||||
pid_m = first_pid_m + (pid % group_size_m)
|
||||
pid_k = (pid % num_pid_in_group) // group_size_m
|
||||
|
||||
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
|
||||
offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
|
||||
offs_n = tl.arange(0, BLOCK_SIZE_N)
|
||||
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
|
||||
a_mask = (offs_am[:, None] < M)
|
||||
# b_ptrs is set up such that it repeats elements along the K axis 8 times
|
||||
b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
|
||||
g_ptrs = g_ptr + offs_bk
|
||||
g_idx = tl.load(g_ptrs)
|
||||
|
||||
# shifter is used to extract the N bits of each element in the 32-bit word from B
|
||||
scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales
|
||||
zeros_ptrs = zeros_ptr + (offs_n[None, :]// infearure_per_bits) + g_idx[:, None] * stride_zeros
|
||||
|
||||
shifter = (offs_bk % infearure_per_bits) * bits
|
||||
zeros_shifter = (offs_n % infearure_per_bits) * bits
|
||||
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)
|
||||
|
||||
for k in range(0, num_pid_n):
|
||||
# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
|
||||
scales = tl.load(scales_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
|
||||
zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
|
||||
|
||||
zeros = (zeros >> zeros_shifter[None, :]) & maxq
|
||||
zeros = (zeros + 1)
|
||||
|
||||
a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
|
||||
b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
|
||||
|
||||
# Now we need to unpack b (which is N-bit values) into 32-bit values
|
||||
b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
|
||||
b = (b - zeros) * scales # Scale and shift
|
||||
b = tl.trans(b)
|
||||
|
||||
accumulator += tl.dot(a, b)
|
||||
a_ptrs += BLOCK_SIZE_N
|
||||
b_ptrs += BLOCK_SIZE_N
|
||||
scales_ptrs += BLOCK_SIZE_N
|
||||
zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
|
||||
|
||||
c = accumulator.to(tl.float16)
|
||||
c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
|
||||
c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
|
||||
tl.store(c_ptrs, accumulator, mask=c_mask)
|
||||
|
||||
|
||||
def triton_matmul(input, qweight, scales, qzeros, g_idx, bits, maxq):
|
||||
output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
|
||||
grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
|
||||
matmul_248_kernel[grid](input, qweight, output,
|
||||
scales, qzeros, g_idx,
|
||||
input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
|
||||
input.stride(0), input.stride(1),
|
||||
qweight.stride(0), qweight.stride(1),
|
||||
output.stride(0), output.stride(1),
|
||||
scales.stride(0), qzeros.stride(0))
|
||||
return output
|
||||
|
||||
Loading…
Reference in New Issue