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108 lines
4.1 KiB
108 lines
4.1 KiB
2 weeks ago
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import time
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import triton
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import triton.language as tl
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from triton.compiler import AttrsDescriptor, ASTSource, compile as triton_compile
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import numpy as np
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from tinygrad import Tensor, dtypes, Device
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from tinygrad.engine.realize import CompiledRunner, ExecItem, ProgramSpec
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from tinygrad.helpers import getenv
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np.set_printoptions(suppress=True)
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@triton.jit
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def matmul_kernel(c_ptr, a_ptr, b_ptr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr):
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pid_m = tl.program_id(axis=0)
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pid_n = tl.program_id(axis=1)
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M, N, K = 4096, 4096, 4096
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stride_am = 4096
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stride_ak = 1
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stride_bk = 4096
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stride_bn = 1
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stride_cm = 4096
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stride_cn = 1
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offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
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offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % 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)
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b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
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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, tl.cdiv(K, BLOCK_SIZE_K)):
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a = tl.load(a_ptrs)
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b = tl.load(b_ptrs)
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accumulator = tl.dot(a, b, accumulator)
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a_ptrs += BLOCK_SIZE_K * stride_ak
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b_ptrs += BLOCK_SIZE_K * stride_bk
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c = tl.cast(accumulator, tl.float16)
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offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
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tl.store(c_ptrs, c)
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# CUDA=1 CUDA_PTX=1 python3 extra/gemm/triton_nv_matmul.py
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if __name__ == "__main__":
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BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K = 64, 128, 64
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M, N, K = 4096, 4096, 4096
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# **** torch test ****
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if getenv("TORCH"):
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import torch
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c = torch.empty((M, N), device='cuda:0', dtype=torch.float16)
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a = torch.empty((M, K), device='cuda:0', dtype=torch.float16)
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b = torch.empty((K, N), device='cuda:0', dtype=torch.float16)
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for i in range(5):
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st = time.perf_counter()
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matmul_kernel[triton.cdiv(M, BLOCK_SIZE_M), triton.cdiv(N, BLOCK_SIZE_N)](
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c, a, b, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K)
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torch.cuda.synchronize()
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et = time.perf_counter() - st
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print(f"TFLOPS {2*M*N*K*1e-12/et:.2f}")
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# **** tinygrad test ****
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compiled = triton_compile(ASTSource(matmul_kernel, "*fp16,*fp16,*fp16",
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attrs=AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=()),
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constants={"BLOCK_SIZE_M": BLOCK_SIZE_M, "BLOCK_SIZE_N": BLOCK_SIZE_N, "BLOCK_SIZE_K": BLOCK_SIZE_K}))
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print(compiled.metadata)
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A, B = Tensor.normal(M, K, std=1e-1, dtype=dtypes.float16).realize(), Tensor.normal(K, N, std=1e-1, dtype=dtypes.float16).realize()
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C = A.matmul(B)
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sched = C.schedule()
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si = sched[-1]
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src = compiled.asm["ptx"]
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# specify the shared memory here so we don't need to do it dynamically
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src = src.replace(".extern .shared .align 16 .b8 global_smem[];", f".shared .align 16 .b8 global_smem[{compiled.metadata.shared}];")
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# useless comment spam
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src = src.replace("\t// begin inline asm\n", "")
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src = src.replace("\t// end inline asm\n", "")
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# remove debug sections
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src = src.split("\t.file")[0]
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assert '.extern .shared' not in src
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prg = ProgramSpec("matmul_kernel", src, device=Device.DEFAULT,
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global_size=[M//BLOCK_SIZE_M, N//BLOCK_SIZE_N, 1], local_size=[32*compiled.metadata.num_warps, 1, 1],
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mem_estimate=A.nbytes() + B.nbytes() + C.nbytes())
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ei = ExecItem(CompiledRunner(prg), [x.ensure_allocated() for x in si.bufs], si.metadata)
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tflops = []
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for i in range(5):
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tm = ei.run(wait=True)
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tflops.append((2*M*K*N/tm)*1e-12)
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print(f"TFLOPS: {max(tflops):.2f}")
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# check correctness
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if getenv("VERIFY"):
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from tinygrad.engine.realize import run_schedule
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triton_buf = np.frombuffer(si.bufs[0].as_buffer(), np.float16).reshape(M,N)
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print(triton_buf)
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run_schedule(sched)
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tinygrad_buf = np.frombuffer(si.bufs[0].as_buffer(), np.float16).reshape(M,N)
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print(tinygrad_buf)
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np.testing.assert_allclose(triton_buf, tinygrad_buf)
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print("correct!")
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